1
|
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.
Collapse
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
| |
Collapse
|
2
|
Schalkamp AK, Harrison NA, Peall KJ, Sandor C. Digital outcome measures from smartwatch data relate to non-motor features of Parkinson's disease. NPJ Parkinsons Dis 2024; 10:110. [PMID: 38811633 PMCID: PMC11137004 DOI: 10.1038/s41531-024-00719-w] [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: 09/12/2023] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
Monitoring of Parkinson's disease (PD) has seen substantial improvement over recent years as digital sensors enable a passive and continuous collection of information in the home environment. However, the primary focus of this work has been motor symptoms, with little focus on the non-motor aspects of the disease. To address this, we combined longitudinal clinical non-motor assessment data and digital multi-sensor data from the Verily Study Watch for 149 participants from the Parkinson's Progression Monitoring Initiative (PPMI) cohort with a diagnosis of PD. We show that digitally collected physical activity and sleep measures significantly relate to clinical non-motor assessments of cognitive, autonomic, and daily living impairment. However, the poor predictive performance we observed, highlights the need for better targeted digital outcome measures to enable monitoring of non-motor symptoms.
Collapse
Affiliation(s)
- Ann-Kathrin Schalkamp
- Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff, United Kingdom
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom
- Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Neil A Harrison
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, United Kingdom
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
| | - Kathryn J Peall
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, United Kingdom.
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, United Kingdom.
| | - Cynthia Sandor
- Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff, United Kingdom.
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom.
- Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom.
| |
Collapse
|
3
|
Kamo H, Oyama G, Yamasaki Y, Nagayama T, Nawashiro R, Hattori N. A proof of concept: digital diary using 24-hour monitoring using wearable device for patients with Parkinson's disease in nursing homes. Front Neurol 2024; 15:1356042. [PMID: 38660090 PMCID: PMC11041395 DOI: 10.3389/fneur.2024.1356042] [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: 12/14/2023] [Accepted: 02/26/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction In the advanced stages of Parkinson's disease (PD), motor complications such as wearing-off and dyskinesia are problematic and vary daily. These symptoms need to be monitored precisely to provide adequate care for patients with advanced PD. Methods This study used wearable devices to explore biomarkers for motor complications by measuring multiple biomarkers in patients with PD residing in facilities and combining them with lifestyle and clinical assessments. Data on the pulse rate and activity index (metabolic equivalents) were collected from 12 patients over 30 days. Results The pulse rate and activity index during the off- and on-periods and dyskinesia were analyzed for two participants; the pulse rate and activity index did not show any particular trend in each participant; however, the pulse rate/activity index was significantly greater in the off-state compared to that in the dyskinesia and on-states, and this index in the dyskinesia state was significantly greater than that in the on-state in both participants. Conclusion These results suggest the pulse rate and activity index combination would be a useful indicator of wearing-off and dyskinesia and that biometric information from wearable devices may function as a digital diary. Accumulating more cases and collecting additional data are necessary to verify our findings.
Collapse
Affiliation(s)
- Hikaru Kamo
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Genko Oyama
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Home Medical Care System, Based on Information and Communication Technology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Drug Development for Parkinson's Disease, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of PRO-Based Integrated Data Analysis in Neurological Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Research and Therapeutics for Movement Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yui Yamasaki
- Sunwels Company Limited, Chiyoda-ku, Tokyo, Japan
| | | | | | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Home Medical Care System, Based on Information and Communication Technology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Drug Development for Parkinson's Disease, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of PRO-Based Integrated Data Analysis in Neurological Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Research and Therapeutics for Movement Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Research Institute of Disease of Old Age, Graduate School of Medicine, Juntendo University, Tokyo, Japan
- Neurodegenerative Disorders Collaborative Laboratory, RIKEN Center for Brain Science, Saitama, Japan
| |
Collapse
|
4
|
Bremm RP, Pavelka L, Garcia MM, Mombaerts L, Krüger R, Hertel F. Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:2195. [PMID: 38610406 PMCID: PMC11014392 DOI: 10.3390/s24072195] [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: 02/28/2024] [Revised: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024]
Abstract
Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson's disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments.
Collapse
Affiliation(s)
- Rene Peter Bremm
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg (F.H.)
| | - Lukas Pavelka
- Parkinson’s Research Clinic, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg; (L.P.); (R.K.)
- Translational Neuroscience, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, 1445 Strassen, Luxembourg
| | - Maria Moscardo Garcia
- Systems Control, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
| | - Laurent Mombaerts
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg (F.H.)
| | - Rejko Krüger
- Parkinson’s Research Clinic, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg; (L.P.); (R.K.)
- Translational Neuroscience, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, 1445 Strassen, Luxembourg
| | - Frank Hertel
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg (F.H.)
| |
Collapse
|
5
|
Thawani SP, Minen MT, Grossman SN, Friedman S, Bhatt JM, Foo FYA, Torres DM, Weinberg HJ, Kim NH, Levitan V, Cardiel MI, Zakin E, Conway JM, Kurzweil AM, Hasanaj L, Stainman RS, Seixas A, Galetta SL, Balcer LJ, Busis NA. A Comparison of Patients' and Neurologists' Assessments of their Teleneurology Encounter: A Cross-Sectional Analysis. Telemed J E Health 2024; 30:841-849. [PMID: 37624656 DOI: 10.1089/tmj.2023.0168] [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: 08/27/2023] Open
Abstract
Background and Objectives: To better understand patients' and neurologists' assessments of their experiences regarding effectiveness of teleneurology encounters. Methods: Following an audio-video telehealth visit, neurologists asked patients to participate in a survey-based research study about the encounter, and then, the neurologists also recorded their own evaluations. Data were analyzed using standard quantitative and qualitative techniques for dichotomous and ordered-category survey responses in this cross-sectional analysis. Results: The study included unique encounters between 187 patients and 11 general neurologists. The mean patient age was 49 ± 17.5 years. Two thirds of the patients (66.8%, 125/187) were female. One third (33.2%; 62) were patients new to the NYU Langone Health neurology practices. The most common patient chief complaints were headache (69/187, 36.9%), focal and generalized numbness or tingling (21, 11.2%), memory difficulty (15, 8%), spine-related symptoms (12, 6.4%), and vertigo (11, 5.9%). Most patients (94.7%, 177/187) reported that the teleneurology encounter satisfied their needs. Patients and their neurologists agreed that the experience was effective in 91% (162/178) of encounters, regardless of whether the visit was for a new or established patient visit. Discussion: More than 90% of new and established patients and their neurologists agreed that teleneurology encounters were effective despite some limitations of the examination, the occasional need for patient assistance, and technical difficulties. Our results provide further evidence to justify and to expand the clinical use of teleneurology.
Collapse
Affiliation(s)
- Sujata P Thawani
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Mia T Minen
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA
| | - Scott N Grossman
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Steven Friedman
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA
| | - Jaydeep M Bhatt
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Farng-Yang A Foo
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Daniel M Torres
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Harold J Weinberg
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Nina H Kim
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Valeriya Levitan
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Myrna I Cardiel
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Elina Zakin
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Jenna M Conway
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Arielle M Kurzweil
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Lisena Hasanaj
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Rebecca S Stainman
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Azizi Seixas
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA
- Department of Informatics and Health Data Science, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Steven L Galetta
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
- Department of Ophthalmology, NYU Grossman School of Medicine, New York, New York, USA
| | - Laura J Balcer
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA
- Department of Ophthalmology, NYU Grossman School of Medicine, New York, New York, USA
| | - Neil A Busis
- Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| |
Collapse
|
6
|
Schmidt SL, Chowdhury AH, Mitchell KT, Peters JJ, Gao Q, Lee HJ, Genty K, Chow SC, Grill WM, Pajic M, Turner DA. At home adaptive dual target deep brain stimulation in Parkinson's disease with proportional control. Brain 2024; 147:911-922. [PMID: 38128546 PMCID: PMC10907084 DOI: 10.1093/brain/awad429] [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: 05/22/2023] [Revised: 10/24/2023] [Accepted: 11/18/2023] [Indexed: 12/23/2023] Open
Abstract
Continuous deep brain stimulation (cDBS) of the subthalamic nucleus (STN) or globus pallidus is an effective treatment for the motor symptoms of Parkinson's disease. The relative benefit of one region over the other is of great interest but cannot usually be compared in the same patient. Simultaneous DBS of both regions may synergistically increase the therapeutic benefit. Continuous DBS is limited by a lack of responsiveness to dynamic, fluctuating symptoms intrinsic to the disease. Adaptive DBS (aDBS) adjusts stimulation in response to biomarkers to improve efficacy, side effects, and efficiency. We combined bilateral DBS of both STN and globus pallidus (dual target DBS) in a prospective within-participant, clinical trial in six patients with Parkinson's disease (n = 6, 55-65 years, n = 2 females). Dual target cDBS was tested for Parkinson's disease symptom control annually over 2 years, measured by motor rating scales, on time without dyskinesia, and medication reduction. Random amplitude experiments probed system dynamics to estimate parameters for aDBS. We then implemented proportional-plus-integral aDBS using a novel distributed (off-implant) architecture. In the home setting, we collected tremor and dyskinesia scores as well as individualized β and DBS amplitudes. Dual target cDBS reduced motor symptoms as measured by Unified Parkinson's Disease Rating Scale (UPDRS) to a greater degree than either region alone (P < 0.05, linear mixed model) in the cohort. The amplitude of β-oscillations in the STN correlated to the speed of hand grasp movements for five of six participants (P < 0.05, Pearson correlation). Random amplitude experiments provided insight into temporal windowing to avoid stimulation artefacts and demonstrated a correlation between STN β amplitude and DBS amplitude. Proportional plus integral control of aDBS reduced average power, while preserving UPDRS III scores in the clinic (P = 0.28, Wilcoxon signed rank), and tremor and dyskinesia scores during blinded testing at home (n = 3, P > 0.05, Wilcoxon ranked sum). In the home setting, DBS power reductions were slight but significant. Dual target cDBS may offer an improvement in treatment of motor symptoms of Parkinson's disease over DBS of either the STN or globus pallidus alone. When combined with proportional plus integral aDBS, stimulation power may be reduced, while preserving the increased benefit of dual target DBS.
Collapse
Affiliation(s)
- Stephen L Schmidt
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Afsana H Chowdhury
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Kyle T Mitchell
- Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
| | - Jennifer J Peters
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Qitong Gao
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Hui-Jie Lee
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA
| | - Katherine Genty
- Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA
| | - Shein-Chung Chow
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
- Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Miroslav Pajic
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Dennis A Turner
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA
| |
Collapse
|
7
|
Willemse IHJ, Schootemeijer S, van den Bergh R, Dawes H, Nonnekes JH, van de Warrenburg BPC. Smartphone applications for Movement Disorders: Towards collaboration and re-use. Parkinsonism Relat Disord 2024; 120:105988. [PMID: 38184466 DOI: 10.1016/j.parkreldis.2023.105988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/20/2023] [Accepted: 12/31/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Numerous smartphone and tablet applications (apps) are available to monitor movement disorders, but an overview of their purpose and stage of development is missing. OBJECTIVES To systematically review published literature and classify smartphone and tablet apps with objective measurement capabilities for the diagnosis, monitoring, assessment, or treatment of movement disorders. METHODS We systematically searched for publications covering smartphone or tablet apps to monitor movement disorders until November 22nd, 2023. We reviewed the target population, measured domains, purpose, and technology readiness level (TRL) of the proposed app and checked their availability in common app stores. RESULTS We identified 113 apps. Most apps were developed for Parkinson's disease specifically (n = 82; 73%) or for movement disorders in general (n = 17; 15%). Apps were either designed to momentarily assess symptoms (n = 65; 58%), support treatment (n = 22; 19%), aid in diagnosis (n = 16; 14%), or passively track symptoms (n = 11; 10%). Commonly assessed domains across movement disorders included fine motor skills (n = 34; 30%), gait (n = 36; 32%), and tremor (n = 32; 28%) for the motor domain and cognition (n = 16; 14%) for the non-motor domain. Twenty-six (23%) apps were proof-of-concepts (TRL 1-3), while most apps were tested in a controlled setting (TRL 4-6; n = 63; 56%). Twenty-four apps were tested in their target setting (TRL 7-9) of which 10 were accessible in common app stores or as Android Package. CONCLUSIONS The development of apps strongly gravitates towards Parkinson's disease and a selection of motor symptoms. Collaboration, re-use and further development of existing apps is encouraged to avoid reinventions of the wheel.
Collapse
Affiliation(s)
- Ilse H J Willemse
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands.
| | - Sabine Schootemeijer
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Robin van den Bergh
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Helen Dawes
- NIHR Exeter BRC, Medical School, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Jorik H Nonnekes
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Rehabilitation, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands; Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, the Netherlands
| | - Bart P C van de Warrenburg
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| |
Collapse
|
8
|
Yang YY, Ho MY, Tai CH, Wu RM, Kuo MC, Tseng YJ. FastEval Parkinsonism: an instant deep learning-assisted video-based online system for Parkinsonian motor symptom evaluation. NPJ Digit Med 2024; 7:31. [PMID: 38332372 PMCID: PMC10853559 DOI: 10.1038/s41746-024-01022-x] [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: 09/27/2023] [Accepted: 01/18/2024] [Indexed: 02/10/2024] Open
Abstract
The Motor Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is designed to assess bradykinesia, the cardinal symptoms of Parkinson's disease (PD). However, it cannot capture the all-day variability of bradykinesia outside the clinical environment. Here, we introduce FastEval Parkinsonism ( https://fastevalp.cmdm.tw/ ), a deep learning-driven video-based system, providing users to capture keypoints, estimate the severity, and summarize in a report. Leveraging 840 finger-tapping videos from 186 individuals (103 patients with Parkinson's disease (PD), 24 participants with atypical parkinsonism (APD), 12 elderly with mild parkinsonism signs (MPS), and 47 healthy controls (HCs)), we employ a dilated convolution neural network with two data augmentation techniques. Our model achieves acceptable accuracies (AAC) of 88.0% and 81.5%. The frequency-intensity (FI) value of thumb-index finger distance was indicated as a pivotal hand parameter to quantify the performance. Our model also shows the usability for multi-angle videos, tested in an external database enrolling over 300 PD patients.
Collapse
Affiliation(s)
- Yu-Yuan Yang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC
| | - Ming-Yang Ho
- Department of Computer Science and Information Engineering, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC
| | - Chung-Hwei Tai
- Department of Neurology, National Taiwan University Hospital, No. 1 Changde St., Zhongzheng Dist., Taipei City, 100229, Taiwan, ROC
| | - Ruey-Meei Wu
- Department of Medicine, National Taiwan University Cancer Center, No. 57, Lane 155, Sec. 3, Keelung Rd., Da'an Dist., Taipei City, 106, Taiwan, ROC
| | - Ming-Che Kuo
- Department of Neurology, National Taiwan University Hospital, No. 1 Changde St., Zhongzheng Dist., Taipei City, 100229, Taiwan, ROC.
- Department of Medicine, National Taiwan University Cancer Center, No. 57, Lane 155, Sec. 3, Keelung Rd., Da'an Dist., Taipei City, 106, Taiwan, ROC.
| | - Yufeng Jane Tseng
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC.
- Department of Computer Science and Information Engineering, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC.
| |
Collapse
|
9
|
Paredes-Acuna N, Utpadel-Fischler D, Ding K, Thakor NV, Cheng G. Upper limb intention tremor assessment: opportunities and challenges in wearable technology. J Neuroeng Rehabil 2024; 21:8. [PMID: 38218890 PMCID: PMC10787996 DOI: 10.1186/s12984-023-01302-9] [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: 08/02/2023] [Accepted: 12/26/2023] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature. METHODOLOGY & RESULTS This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table. CONCLUSIONS Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.
Collapse
Affiliation(s)
- Natalia Paredes-Acuna
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany.
| | - Daniel Utpadel-Fischler
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Keqin Ding
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Gordon Cheng
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany
| |
Collapse
|
10
|
Feldmann LK, Roudini J, Kühn AA, Habets JGV. Improving naturalistic neuroscience with patient engagement strategies. Front Hum Neurosci 2024; 17:1325154. [PMID: 38259336 PMCID: PMC10800538 DOI: 10.3389/fnhum.2023.1325154] [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: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction The clinical implementation of chronic electrophysiology-driven adaptive deep brain stimulation (DBS) algorithms in movement disorders requires reliable representation of motor and non-motor symptoms in electrophysiological biomarkers, throughout normal life (naturalistic). To achieve this, there is the need for high-resolution and -quality chronic objective and subjective symptom monitoring in parallel to biomarker recordings. To realize these recordings, an active participation and engagement of the investigated patients is necessary. To date, there has been little research into patient engagement strategies for DBS patients or chronic electrophysiological recordings. Concepts and results We here present our concept and the first results of a patient engagement strategy for a chronic DBS study. After discussing the current state of literature, we present objectives, methodology and consequences of the patient engagement regarding study design, data acquisition, and study infrastructure. Nine patients with Parkinson's disease and their caregivers participated in the meeting, and their input led to changes to our study design. Especially, the patient input helped us designing study-set-up meetings and support structures. Conclusion We believe that patient engagement increases compliance and study motivation through scientific empowerment of patients. While considering patient opinion on sensors or questionnaire questions may lead to more precise and reliable data acquisition, there was also a high demand for study support and engagement structures. Hence, we recommend the implementation of patient engagement in planning of chronic studies with complex designs, long recording durations or high demand for individual active study participation.
Collapse
Affiliation(s)
- Lucia K. Feldmann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Juliet Roudini
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité, Berlin, Germany
- Patient and Stakeholder Engagement, Cluster of Excellence, NeuroCure, Berlin, Germany
| | - Andrea A. Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Charité University Medicine, Berlin, Germany
- NeuroCure Clinical Research Center, Charité University Medicine, Berlin, Germany
- DZNE, German Center for Neurodegenerative Diseases, Berlin, Germany
| | - Jeroen G. V. Habets
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| |
Collapse
|
11
|
Varghese J, Brenner A, Fujarski M, van Alen CM, Plagwitz L, Warnecke T. Machine Learning in the Parkinson's disease smartwatch (PADS) dataset. NPJ Parkinsons Dis 2024; 10:9. [PMID: 38182602 PMCID: PMC10770131 DOI: 10.1038/s41531-023-00625-7] [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: 06/08/2023] [Accepted: 12/18/2023] [Indexed: 01/07/2024] Open
Abstract
The utilisation of smart devices, such as smartwatches and smartphones, in the field of movement disorders research has gained significant attention. However, the absence of a comprehensive dataset with movement data and clinical annotations, encompassing a wide range of movement disorders including Parkinson's disease (PD) and its differential diagnoses (DD), presents a significant gap. The availability of such a dataset is crucial for the development of reliable machine learning (ML) models on smart devices, enabling the detection of diseases and monitoring of treatment efficacy in a home-based setting. We conducted a three-year cross-sectional study at a large tertiary care hospital. A multi-modal smartphone app integrated electronic questionnaires and smartwatch measures during an interactive assessment designed by neurologists to provoke subtle changes in movement pathologies. We captured over 5000 clinical assessment steps from 504 participants, including PD, DD, and healthy controls (HC). After age-matching, an integrative ML approach combining classical signal processing and advanced deep learning techniques was implemented and cross-validated. The models achieved an average balanced accuracy of 91.16% in the classification PD vs. HC, while PD vs. DD scored 72.42%. The numbers suggest promising performance while distinguishing similar disorders remains challenging. The extensive annotations, including details on demographics, medical history, symptoms, and movement steps, provide a comprehensive database to ML techniques and encourage further investigations into phenotypical biomarkers related to movement disorders.
Collapse
Affiliation(s)
- Julian Varghese
- Institute of Medical Informatics, University of Münster, Münster, Germany.
- European Research Centre of Information Systems, University of Münster, Münster, Germany.
| | - Alexander Brenner
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Michael Fujarski
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | | | - Lucas Plagwitz
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Tobias Warnecke
- Department of Neurology and Neurorehabilitation, Klinikum Osnabrück - Academic teaching hospital of the University of Münster, Osnabrück, Germany
| |
Collapse
|
12
|
Fay-Karmon T, Galor N, Heimler B, Zilka A, Bartsch RP, Plotnik M, Hassin-Baer S. Home-based monitoring of persons with advanced Parkinson's disease using smartwatch-smartphone technology. Sci Rep 2024; 14:9. [PMID: 38167434 PMCID: PMC10761812 DOI: 10.1038/s41598-023-48209-y] [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: 05/14/2023] [Accepted: 11/23/2023] [Indexed: 01/05/2024] Open
Abstract
Movement deterioration is the hallmark of Parkinson's disease (PD), characterized by levodopa-induced motor-fluctuations (i.e., symptoms' variability related to the medication cycle) in advanced stages. However, motor symptoms are typically too sporadically and/or subjectively assessed, ultimately preventing the effective monitoring of their progression, and thus leading to suboptimal treatment/therapeutic choices. Smartwatches (SW) enable a quantitative-oriented approach to motor-symptoms evaluation, namely home-based monitoring (HBM) using an embedded inertial measurement unit. Studies validated such approach against in-clinic evaluations. In this work, we aimed at delineating personalized motor-fluctuations' profiles, thus capturing individual differences. 21 advanced PD patients with motor fluctuations were monitored for 2 weeks using a SW and a smartphone-dedicated app (Intel Pharma Analytics Platform). The SW continuously collected passive data (tremor, dyskinesia, level of activity using dedicated algorithms) and active data, i.e., time-up-and-go, finger tapping, hand tremor and hand rotation carried out daily, once in OFF and once in ON levodopa periods. We observed overall high compliance with the protocol. Furthermore, we observed striking differences among the individual patterns of symptoms' levodopa-related variations across the HBM, allowing to divide our participants among four data-driven, motor-fluctuations' profiles. This highlights the potential of HBM using SW technology for revolutionizing clinical practices.
Collapse
Affiliation(s)
- Tsviya Fay-Karmon
- Movement Disorders Institute, Department of Neurology, Sheba Medical Center, Ramat Gan, Israel
| | - Noam Galor
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
| | - Benedetta Heimler
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
| | - Asaf Zilka
- Movement Disorders Institute, Department of Neurology, Sheba Medical Center, Ramat Gan, Israel
| | - Ronny P Bartsch
- Department of Physics, Bar-Ilan University, Ramat Gan, Israel
| | - Meir Plotnik
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Sharon Hassin-Baer
- Movement Disorders Institute, Department of Neurology, Sheba Medical Center, Ramat Gan, Israel.
- Department of Neurology and Neurosurgery, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| |
Collapse
|
13
|
Sabo A, Iaboni A, Taati B, Fasano A, Gorodetsky C. Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson's disease. Biomed Eng Online 2023; 22:120. [PMID: 38082277 PMCID: PMC10714555 DOI: 10.1186/s12938-023-01175-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 11/19/2023] [Indexed: 12/18/2023] Open
Abstract
INTRODUCTION Gait impairments in Parkinson's disease (PD) are treated with dopaminergic medication or deep-brain stimulation (DBS), although the magnitude of the response is variable between individuals. Computer vision-based approaches have previously been evaluated for measuring the severity of parkinsonian gait in videos, but have not been evaluated for their ability to identify changes within individuals in response to treatment. This pilot study examines whether a vision-based model, trained on videos of parkinsonism, is able to detect improvement in parkinsonian gait in people with PD in response to medication and DBS use. METHODS A spatial-temporal graph convolutional model was trained to predict MDS-UPDRS-gait scores in 362 videos from 14 older adults with drug-induced parkinsonism. This model was then used to predict MDS-UPDRS-gait scores on a different dataset of 42 paired videos from 13 individuals with PD, recorded while ON and OFF medication and DBS treatment during the same clinical visit. Statistical methods were used to assess whether the model was responsive to changes in gait in the ON and OFF states. RESULTS The MDS-UPDRS-gait scores predicted by the model were lower on average (representing improved gait; p = 0.017, Cohen's d = 0.495) during the ON medication and DBS treatment conditions. The magnitude of the differences between ON and OFF state was significantly correlated between model predictions and clinician annotations (p = 0.004). The predicted scores were significantly correlated with the clinician scores (Kendall's tau-b = 0.301, p = 0.010), but were distributed in a smaller range as compared to the clinician scores. CONCLUSION A vision-based model trained on parkinsonian gait did not accurately predict MDS-UPDRS-gait scores in a different PD cohort, but detected weak, but statistically significant proportional changes in response to medication and DBS use. Large, clinically validated datasets of videos captured in many different settings and treatment conditions are required to develop accurate vision-based models of parkinsonian gait.
Collapse
Affiliation(s)
- Andrea Sabo
- KITE, Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
| | - Andrea Iaboni
- KITE, Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
- Department of Psychiatry, University of Toronto, 250 College Street, 8th Floor, Toronto, ON, M5T 1R8, Canada
- Centre for Mental Health, University Health Network, 33 Russell Street, Toronto, ON, M5S 2S1, Canada
| | - Babak Taati
- KITE, Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
- Department of Computer Science, University of Toronto, 10 King's College Road, Room 3302, Toronto, ON, M5S 3G4, Canada
- Institute of Biomedical Engineering, University of Toronto, 164 College Street. Room 407, Toronto, ON, M2S 3G9, Canada
- Vector Institute, 661 University Ave Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Alfonso Fasano
- KITE, Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
- Division of Neurology, The Hospital for Sick Children, University of Toronto, 555 University Avenue, Toronto, ON, M5G 1X8, Canada
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Toronto, ON, Canada
- Krembil Brain Institute, Toronto, ON, Canada
- CenteR for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
| | - Carolina Gorodetsky
- Division of Neurology, The Hospital for Sick Children, University of Toronto, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Toronto, ON, Canada.
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
Wilkins KB, Melbourne JA, Akella P, Bronte-Stewart HM. Unraveling the complexities of programming neural adaptive deep brain stimulation in Parkinson's disease. Front Hum Neurosci 2023; 17:1310393. [PMID: 38094147 PMCID: PMC10716917 DOI: 10.3389/fnhum.2023.1310393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/09/2023] [Indexed: 02/01/2024] Open
Abstract
Over the past three decades, deep brain stimulation (DBS) for Parkinson's disease (PD) has been applied in a continuous open loop fashion, unresponsive to changes in a given patient's state or symptoms over the course of a day. Advances in recent neurostimulator technology enable the possibility for closed loop adaptive DBS (aDBS) for PD as a treatment option in the near future in which stimulation adjusts in a demand-based manner. Although aDBS offers great clinical potential for treatment of motor symptoms, it also brings with it the need for better understanding how to implement it in order to maximize its benefits. In this perspective, we outline considerations for programing several key parameters for aDBS based on our experience across several aDBS-capable research neurostimulators. At its core, aDBS hinges on successful identification of relevant biomarkers that can be measured reliably in real-time working in cohesion with a control policy that governs stimulation adaption. However, auxiliary parameters such as the window in which stimulation is allowed to adapt, as well as the rate it changes, can be just as impactful on performance and vary depending on the control policy and patient. A standardize protocol for programming aDBS will be crucial to ensuring its effective application in clinical practice.
Collapse
Affiliation(s)
- Kevin B. Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Jillian A. Melbourne
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Pranav Akella
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Helen M. Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
| |
Collapse
|
16
|
Chen E, Prakash S, Janapa Reddi V, Kim D, Rajpurkar P. A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring. Nat Biomed Eng 2023:10.1038/s41551-023-01115-0. [PMID: 37932379 DOI: 10.1038/s41551-023-01115-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 09/26/2023] [Indexed: 11/08/2023]
Abstract
The complex relationships between continuously monitored health signals and therapeutic regimens can be modelled via machine learning. However, the clinical implementation of the models will require changes to clinical workflows. Here we outline ClinAIOps ('clinical artificial-intelligence operations'), a framework that integrates continuous therapeutic monitoring and the development of artificial intelligence (AI) for clinical care. ClinAIOps leverages three feedback loops to enable the patient to make treatment adjustments using AI outputs, the clinician to oversee patient progress with AI assistance, and the AI developer to receive continuous feedback from both the patient and the clinician. We lay out the central challenges and opportunities in the deployment of ClinAIOps by means of examples of its application in the management of blood pressure, diabetes and Parkinson's disease. By enabling more frequent and accurate measurements of a patient's health and more timely adjustments to their treatment, ClinAIOps may substantially improve patient outcomes.
Collapse
Affiliation(s)
- Emma Chen
- Harvard John A. Paulson School of Engineering and Applied Sciences, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Shvetank Prakash
- Harvard John A. Paulson School of Engineering and Applied Sciences, Boston, MA, USA
| | - Vijay Janapa Reddi
- Harvard John A. Paulson School of Engineering and Applied Sciences, Boston, MA, USA
| | - David Kim
- Department of Emergency Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
17
|
Oliveira AM, Coelho L, Carvalho E, Ferreira-Pinto MJ, Vaz R, Aguiar P. Machine learning for adaptive deep brain stimulation in Parkinson's disease: closing the loop. J Neurol 2023; 270:5313-5326. [PMID: 37530789 PMCID: PMC10576725 DOI: 10.1007/s00415-023-11873-1] [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: 05/09/2023] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 08/03/2023]
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease bearing a severe social and economic impact. So far, there is no known disease modifying therapy and the current available treatments are symptom oriented. Deep Brain Stimulation (DBS) is established as an effective treatment for PD, however current systems lag behind today's technological potential. Adaptive DBS, where stimulation parameters depend on the patient's physiological state, emerges as an important step towards "smart" DBS, a strategy that enables adaptive stimulation and personalized therapy. This new strategy is facilitated by currently available neurotechnologies allowing the simultaneous monitoring of multiple signals, providing relevant physiological information. Advanced computational models and analytical methods are an important tool to explore the richness of the available data and identify signal properties to close the loop in DBS. To tackle this challenge, machine learning (ML) methods applied to DBS have gained popularity due to their ability to make good predictions in the presence of multiple variables and subtle patterns. ML based approaches are being explored at different fronts such as the identification of electrophysiological biomarkers and the development of personalized control systems, leading to effective symptom relief. In this review, we explore how ML can help overcome the challenges in the development of closed-loop DBS, particularly its role in the search for effective electrophysiology biomarkers. Promising results demonstrate ML potential for supporting a new generation of adaptive DBS, with better management of stimulation delivery, resulting in more efficient and patient-tailored treatments.
Collapse
Affiliation(s)
- Andreia M Oliveira
- Faculdade de Engenharia da Universidade do Porto, Porto, Portugal
- Neuroengineering and Computational Neuroscience Lab, Instituto de Investigação e Inovação da Universidade do Porto, Porto, Portugal
| | - Luis Coelho
- Instituto Superior de Engenharia do Porto, Porto, Portugal
| | - Eduardo Carvalho
- Neuroengineering and Computational Neuroscience Lab, Instituto de Investigação e Inovação da Universidade do Porto, Porto, Portugal
- ICBAS-School of Medicine and Biomedical Sciences, University of Porto, Porto, Portugal
| | - Manuel J Ferreira-Pinto
- Centro Hospitalar Universitário de São João, Porto, Portugal
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Rui Vaz
- Centro Hospitalar Universitário de São João, Porto, Portugal
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Paulo Aguiar
- Faculdade de Engenharia da Universidade do Porto, Porto, Portugal.
- Neuroengineering and Computational Neuroscience Lab, Instituto de Investigação e Inovação da Universidade do Porto, Porto, Portugal.
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal.
- i3S-Instituto de Investigação e Inovação em Saúde, Rua Alfredo Allen, 208, 4200-135, Porto, Portugal.
| |
Collapse
|
18
|
Bronte-Stewart H, Gala A, Wilkins K, Pettruci M, Kehnemouyi Y, Velisar A, Trager M. The digital signature of emergent tremor in Parkinson's disease. RESEARCH SQUARE 2023:rs.3.rs-3467667. [PMID: 37961117 PMCID: PMC10635351 DOI: 10.21203/rs.3.rs-3467667/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Emergent tremor in Parkinson's disease (PD) can occur during sustained postures or movement that is 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. Objective Investigate whether emergent tremor during repetitive alternating finger tapping (RAFT) on a quantitative digitography (QDG) device can be reliably identified and distinguished from voluntary tapping. Methods 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 labelled 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. Results 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). Conclusions 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.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Megan Trager
- Columbia University College of Physicians and Surgeons
| |
Collapse
|
19
|
van der Linden C, Berger T, Brandt GA, Strelow JN, Jergas H, Baldermann JC, Visser-Vandewalle V, Fink GR, Barbe MT, Petry-Schmelzer JN, Dembek TA. Accelerometric Classification of Resting and Postural Tremor Amplitude. SENSORS (BASEL, SWITZERLAND) 2023; 23:8621. [PMID: 37896714 PMCID: PMC10611060 DOI: 10.3390/s23208621] [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: 09/18/2023] [Revised: 10/12/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023]
Abstract
Clinical rating scales for tremors have significant limitations due to low resolution, high rater dependency, and lack of applicability in outpatient settings. Reliable, quantitative approaches for assessing tremor severity are warranted, especially evaluating treatment effects, e.g., of deep brain stimulation (DBS). We aimed to investigate how different accelerometry metrics can objectively classify tremor amplitude of Essential Tremor (ET) and tremor in Parkinson's Disease (PD). We assessed 860 resting and postural tremor trials in 16 patients with ET and 25 patients with PD under different DBS settings. Clinical ratings were compared to different metrics, based on either spectral components in the tremorband or pure acceleration, derived from simultaneous triaxial accelerometry captured at the index finger and wrist. Nonlinear regression was applied to a training dataset to determine the relationship between accelerometry and clinical ratings, which was then evaluated in a holdout dataset. All of the investigated accelerometry metrics could predict clinical tremor ratings with a high concordance (>70%) and substantial interrater reliability (Cohen's weighted Kappa > 0.7) in out-of-sample data. Finger-worn accelerometry performed slightly better than wrist-worn accelerometry. We conclude that triaxial accelerometry reliably quantifies resting and postural tremor amplitude in ET and PD patients. A full release of our dataset and software allows for implementation, development, training, and validation of novel methods.
Collapse
Affiliation(s)
- Christina van der Linden
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany; (C.v.d.L.); (J.N.P.-S.)
| | - Thea Berger
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany; (C.v.d.L.); (J.N.P.-S.)
| | - Gregor A. Brandt
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany; (C.v.d.L.); (J.N.P.-S.)
| | - Joshua N. Strelow
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany; (C.v.d.L.); (J.N.P.-S.)
- Department of Stereotactic and Functional Neurosurgery, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Hannah Jergas
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany; (C.v.d.L.); (J.N.P.-S.)
| | - Juan Carlos Baldermann
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany; (C.v.d.L.); (J.N.P.-S.)
- Department of Psychiatry and Psychotherapy, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
| | - Gereon R. Fink
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany; (C.v.d.L.); (J.N.P.-S.)
- Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Michael T. Barbe
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany; (C.v.d.L.); (J.N.P.-S.)
| | - Jan Niklas Petry-Schmelzer
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany; (C.v.d.L.); (J.N.P.-S.)
| | - Till A. Dembek
- Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany; (C.v.d.L.); (J.N.P.-S.)
| |
Collapse
|
20
|
Löhle M, Timpka J, Bremer A, Khodakarami H, Gandor F, Horne M, Ebersbach G, Odin P, Storch A. Application of single wrist-wearable accelerometry for objective motor diary assessment in fluctuating Parkinson's disease. NPJ Digit Med 2023; 6:194. [PMID: 37848531 PMCID: PMC10582031 DOI: 10.1038/s41746-023-00937-1] [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: 03/30/2023] [Accepted: 09/29/2023] [Indexed: 10/19/2023] Open
Abstract
Advanced Parkinson's disease (PD) is characterized by motor fluctuations including unpredictable oscillations remarkably impairing quality of life. Effective management and development of novel therapies for these response fluctuations largely depend on clinical rating instruments such as the widely-used PD home diary, which are associated with biases and errors. Recent advancements in digital health technologies provide user-friendly wearables that can be tailored for continuous monitoring of motor fluctuations. Their criterion validity under real-world conditions using clinical examination as the gold standard remains to be determined. We prospectively examined this validity of a wearable accelerometer-based digital Parkinson's Motor Diary (adPMD) using the Parkinson's Kinetigraph (PKG®) in an alternative application by converting its continuous data into one of the three motor categories of the PD home diary (Off, On and Dyskinetic state). Sixty-three out of 91 eligible participants with fluctuating PD (46% men, average age 66) had predefined sufficient adPMD datasets (>70% of half-hour periods) from 2 consecutive days. 92% of per-protocol assessments were completed. adPMD monitoring of daily times in motor states showed moderate validity for Off and Dyskinetic state (ICC = 0.43-0.51), while inter-rating methods agreements on half-hour-level can be characterized as poor (median Cohen's κ = 0.13-0.21). Individualization of adPMD thresholds for transferring accelerometer data into diary categories improved temporal agreements up to moderate level for Dyskinetic state detection (median Cohen's κ = 0.25-0.41). Here we report that adPMD real-world-monitoring captures daily times in Off and Dyskinetic state in advanced PD with moderate validities, while temporal agreement of adPMD and clinical observer diary data is limited.
Collapse
Affiliation(s)
- Matthias Löhle
- Department of Neurology, University Medical Center Rostock, Rostock, Germany.
- German Center for Neurodegenerative Diseases (DZNE) Rostock-Greifswald, Rostock, Germany.
| | - Jonathan Timpka
- Division of Neurology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Alexander Bremer
- Department of Neurology, University Medical Center Rostock, Rostock, Germany
| | | | - Florin Gandor
- Movement Disorders Hospital, Beelitz-Heilstätten, Beelitz, Germany
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
| | - Malcom Horne
- Bionics Institute, Melbourne, VIC, Australia
- The Department of Medicine, The University of Melbourne, St Vincent's Hospital, Fitzroy, VIC, 3010, Australia
| | - Georg Ebersbach
- Movement Disorders Hospital, Beelitz-Heilstätten, Beelitz, Germany
| | - Per Odin
- Division of Neurology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Alexander Storch
- Department of Neurology, University Medical Center Rostock, Rostock, Germany.
- German Center for Neurodegenerative Diseases (DZNE) Rostock-Greifswald, Rostock, Germany.
| |
Collapse
|
21
|
Rosch M, Gutowski T, Baehr M, Eggert J, Gottfried K, Gundler C, Nürnberg S, Langebrake C, Dadkhah A. Development of an immediate release excipient composition for 3D printing via direct powder extrusion in a hospital. Int J Pharm 2023; 643:123218. [PMID: 37467818 DOI: 10.1016/j.ijpharm.2023.123218] [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: 05/19/2023] [Revised: 07/07/2023] [Accepted: 07/08/2023] [Indexed: 07/21/2023]
Abstract
3D printing offers the possibility to prepare personalized tablets on demand, making it an intriguing technology for hospital pharmacies. For the implementation of 3D-printed tablets into the digital Closed Loop Medication Management system, the required tablet formulation and development of the manufacturing process as well as the pharmaceutical validation were conducted. The goal of the formulation development was to enable an optimal printing process and rapid dissolution of the printed tablets for the selected model drugs Levodopa/Carbidopa. The 3D printed tablets were prepared by direct powder extrusion. Printability, thermal properties, disintegration, dissolution, physical properties and storage stability were investigated by employing analytical methods such as HPLC-UV, DSC and TGA. The developed formulation shows a high dose accuracy and an immediate drug release for Levodopa. In addition, the tablets exhibit high crushing strength and very low friability. Unfortunately, Carbidopa did not tolerate the printing process. This is the first study to develop an immediate release excipient composition via direct powder extrusion in a hospital pharmacy setting. The developed process is suitable for the implementation in Closed-Loop Medication Management systems in hospital pharmacies and could therefore contribute to medication safety.
Collapse
Affiliation(s)
- Moritz Rosch
- Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias Gutowski
- Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Michael Baehr
- Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Eggert
- Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karl Gottfried
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christopher Gundler
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sylvia Nürnberg
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Claudia Langebrake
- Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Adrin Dadkhah
- Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| |
Collapse
|
22
|
Jha A, Espay AJ, Lees AJ. Digital Biomarkers in Parkinson's Disease: Missing the Forest for the Trees? Mov Disord Clin Pract 2023; 10:S68-S72. [PMID: 37637991 PMCID: PMC10448130 DOI: 10.1002/mdc3.13746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/21/2023] [Accepted: 03/29/2023] [Indexed: 08/29/2023] Open
Affiliation(s)
- Ashwani Jha
- UCL Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Alberto J. Espay
- James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of NeurologyUniversity of CincinnatiCincinnatiOhioUSA
| | - Andrew J. Lees
- Reta Lila Weston Institute of Neurological Studies, Department of Clinical Movement Disorder and Neuroscience, Institute of NeurologyUniversity College LondonLondonUnited Kingdom
| |
Collapse
|
23
|
Zhou Q, Geng Z, Yang L, Shen B, Kan Z, Qi Y, Hu S, Dong B, Bai X, Xu L, Song H, Ren L. A Wearable Healthcare Platform Integrated with Biomimetical Ions Conducted Metal-Organic Framework Composites for Gas and Strain Sensing in Non-Overlapping Mode. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2207663. [PMID: 37078804 PMCID: PMC10288278 DOI: 10.1002/advs.202207663] [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: 12/26/2022] [Revised: 03/10/2023] [Indexed: 05/03/2023]
Abstract
Intelligent wearable devices are essential for telemedicine healthcare as they enable real-time monitoring of physiological information. Elaborately constructing synapse-inspired materials provides a crucial guidance for designing high-performance sensors toward multiplex stimuli response. However, a realistic mimesis both in the "structure and sense" of biological synapses to obtain advanced multi-functions is still challenging but essential for simplifying subsequent circuit and logic programs. Herein, an ionic artificial synapse integrated with Ti3 CNTx nanosheets in situ grown with zeolitic imidazolate framework flowers (ZIF-L@Ti3 CNTx composite) is constructed to concurrently mimic the structure and working mechanism of the synapse. The flexible sensor of the bio-inspired ZIF-L@Ti3 CNTx composite exhibits excellent dual-mode dimethylamine (DMA) and strain-sensitive response with non-overlapping resistance variations. The specific ions conduction working principle triggered by DMA gas or strain with the assistance of humidity is confirmed by the density functional theory simulation. Last, an intelligent wearable system is self-developed by integrating the dual-mode sensor into flexible printed circuits. This device is successfully applied in pluralistic monitoring of abnormal physiological signals of Parkinson's sufferers, including real-time and accurate assessment of simulated DMA expiration and kinematic tremor signals. This work provides a feasible routine to develop intelligent multifunctional devices for upsurging telemedicine diagnosis.
Collapse
Affiliation(s)
- Qingqing Zhou
- State Key Laboratory of Integrated OptoelectronicsCollege of Electronic Science and EngineeringJilin UniversityChangchun130012P. R. China
| | - Zixun Geng
- State Key Laboratory of Integrated OptoelectronicsCollege of Electronic Science and EngineeringJilin UniversityChangchun130012P. R. China
| | - Long Yang
- State Key Laboratory of Integrated OptoelectronicsCollege of Electronic Science and EngineeringJilin UniversityChangchun130012P. R. China
| | - Bo Shen
- State Key Laboratory of Integrated OptoelectronicsCollege of Electronic Science and EngineeringJilin UniversityChangchun130012P. R. China
| | - Zitong Kan
- State Key Laboratory of Integrated OptoelectronicsCollege of Electronic Science and EngineeringJilin UniversityChangchun130012P. R. China
| | - Yu Qi
- State Key Laboratory of Integrated OptoelectronicsCollege of Electronic Science and EngineeringJilin UniversityChangchun130012P. R. China
| | - Songtao Hu
- State Key Laboratory of Integrated OptoelectronicsCollege of Electronic Science and EngineeringJilin UniversityChangchun130012P. R. China
| | - Biao Dong
- State Key Laboratory of Integrated OptoelectronicsCollege of Electronic Science and EngineeringJilin UniversityChangchun130012P. R. China
| | - Xue Bai
- State Key Laboratory of Integrated OptoelectronicsCollege of Electronic Science and EngineeringJilin UniversityChangchun130012P. R. China
| | - Lin Xu
- State Key Laboratory of Integrated OptoelectronicsCollege of Electronic Science and EngineeringJilin UniversityChangchun130012P. R. China
- Key Laboratory of Bionic EngineeringMinistry of EducationCollege of Biological and Agricultural EngineeringJilin UniversityChangchun130025P. R. China
| | - Hongwei Song
- State Key Laboratory of Integrated OptoelectronicsCollege of Electronic Science and EngineeringJilin UniversityChangchun130012P. R. China
| | - Luquan Ren
- Key Laboratory of Bionic EngineeringMinistry of EducationCollege of Biological and Agricultural EngineeringJilin UniversityChangchun130025P. R. China
| |
Collapse
|
24
|
Wolff A, Schumacher NU, Pürner D, Machetanz G, Demleitner AF, Feneberg E, Hagemeier M, Lingor P. Parkinson's disease therapy: what lies ahead? J Neural Transm (Vienna) 2023; 130:793-820. [PMID: 37147404 PMCID: PMC10199869 DOI: 10.1007/s00702-023-02641-6] [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: 02/15/2023] [Accepted: 04/25/2023] [Indexed: 05/07/2023]
Abstract
The worldwide prevalence of Parkinson's disease (PD) has been constantly increasing in the last decades. With rising life expectancy, a longer disease duration in PD patients is observed, further increasing the need and socioeconomic importance of adequate PD treatment. Today, PD is exclusively treated symptomatically, mainly by dopaminergic stimulation, while efforts to modify disease progression could not yet be translated to the clinics. New formulations of approved drugs and treatment options of motor fluctuations in advanced stages accompanied by telehealth monitoring have improved PD patients care. In addition, continuous improvement in the understanding of PD disease mechanisms resulted in the identification of new pharmacological targets. Applying novel trial designs, targeting of pre-symptomatic disease stages, and the acknowledgment of PD heterogeneity raise hopes to overcome past failures in the development of drugs for disease modification. In this review, we address these recent developments and venture a glimpse into the future of PD therapy in the years to come.
Collapse
Affiliation(s)
- Andreas Wolff
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Nicolas U Schumacher
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Dominik Pürner
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Gerrit Machetanz
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Antonia F Demleitner
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Emily Feneberg
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Maike Hagemeier
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Paul Lingor
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
| |
Collapse
|
25
|
Rudd J, Igbrude C. A global perspective on data powering responsible AI solutions in health applications. AI AND ETHICS 2023:1-11. [PMID: 37360149 PMCID: PMC10231277 DOI: 10.1007/s43681-023-00302-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/18/2023] [Indexed: 06/28/2023]
Abstract
Healthcare AI solutions have the potential to transform access, quality of care, and improve outcomes for patients globally. This review suggests consideration of a more global perspective, with a particular focus on marginalized communities, during the development of healthcare AI solutions. The review focuses on one aspect (medical applications) to allow technologists to build solutions in today's environment with an understanding of the challenges they face. The following sections explore and discuss the current challenges in the underlying data and AI technology design on healthcare solutions for global deployment. We highlight some of the factors that lead to gaps in data, gaps around regulations for the healthcare sector, and infrastructural challenges in power and network connectivity, as well as lack of social systems for healthcare and education, which pose challenges to the potential universal impacts of such technologies. We recommend using these considerations in developing prototype healthcare AI solutions to better capture the needs of a global population.
Collapse
|
26
|
Habets JGV, Spooner RK, Mathiopoulou V, Feldmann LK, Busch JL, Roediger J, Bahners BH, Schnitzler A, Florin E, Kühn AA. A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115238. [PMID: 37299968 DOI: 10.3390/s23115238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/24/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
Bradykinesia is a cardinal hallmark of Parkinson's disease (PD). Improvement in bradykinesia is an important signature of effective treatment. Finger tapping is commonly used to index bradykinesia, albeit these approaches largely rely on subjective clinical evaluations. Moreover, recently developed automated bradykinesia scoring tools are proprietary and are not suitable for capturing intraday symptom fluctuation. We assessed finger tapping (i.e., Unified Parkinson's Disease Rating Scale (UPDRS) item 3.4) in 37 people with Parkinson's disease (PwP) during routine treatment follow ups and analyzed their 350 sessions of 10-s tapping using index finger accelerometry. Herein, we developed and validated ReTap, an open-source tool for the automated prediction of finger tapping scores. ReTap successfully detected tapping blocks in over 94% of cases and extracted clinically relevant kinematic features per tap. Importantly, based on the kinematic features, ReTap predicted expert-rated UPDRS scores significantly better than chance in a hold out validation sample (n = 102). Moreover, ReTap-predicted UPDRS scores correlated positively with expert ratings in over 70% of the individual subjects in the holdout dataset. ReTap has the potential to provide accessible and reliable finger tapping scores, either in the clinic or at home, and may contribute to open-source and detailed analyses of bradykinesia.
Collapse
Affiliation(s)
- Jeroen G V Habets
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, Germany
| | - Rachel K Spooner
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Varvara Mathiopoulou
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, Germany
| | - Lucia K Feldmann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, Germany
| | - Johannes L Busch
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, Germany
| | - Jan Roediger
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, Germany
| | - Bahne H Bahners
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, Germany
| |
Collapse
|
27
|
Adams JL, Kangarloo T, Tracey B, O'Donnell P, Volfson D, Latzman RD, Zach N, Alexander R, Bergethon P, Cosman J, Anderson D, Best A, Severson J, Kostrzebski MA, Auinger P, Wilmot P, Pohlson Y, Waddell E, Jensen-Roberts S, Gong Y, Kilambi KP, Herrero TR, Ray Dorsey E. Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study. NPJ Parkinsons Dis 2023; 9:64. [PMID: 37069193 PMCID: PMC10108794 DOI: 10.1038/s41531-023-00497-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 02/27/2023] [Indexed: 04/19/2023] Open
Abstract
Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.
Collapse
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.
| | | | | | - Patricio O'Donnell
- Takeda Pharmaceuticals, Cambridge, MA, USA
- Sage Therapeutics, Seattle, WA, USA
| | | | | | - Neta Zach
- Takeda Pharmaceuticals, Cambridge, MA, USA
| | - Robert Alexander
- Takeda Pharmaceuticals, Cambridge, MA, USA
- Banner Health, Phoenix, AZ, 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
| | - Emma Waddell
- 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
| | - Yishu Gong
- Takeda Pharmaceuticals, Cambridge, MA, USA
| | - Krishna Praneeth Kilambi
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA, 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
| |
Collapse
|
28
|
Keshet A, Reicher L, Bar N, Segal E. Wearable and digital devices to monitor and treat metabolic diseases. Nat Metab 2023; 5:563-571. [PMID: 37100995 DOI: 10.1038/s42255-023-00778-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 03/07/2023] [Indexed: 04/28/2023]
Abstract
Cardiometabolic diseases are a major public-health concern owing to their increasing prevalence worldwide. These diseases are characterized by a high degree of interindividual variability with regards to symptoms, severity, complications and treatment responsiveness. Recent technological advances, and the growing availability of wearable and digital devices, are now making it feasible to profile individuals in ever-increasing depth. Such technologies are able to profile multiple health-related outcomes, including molecular, clinical and lifestyle changes. Nowadays, wearable devices allowing for continuous and longitudinal health screening outside the clinic can be used to monitor health and metabolic status from healthy individuals to patients at different stages of disease. Here we present an overview of the wearable and digital devices that are most relevant for cardiometabolic-disease-related readouts, and how the information collected from such devices could help deepen our understanding of metabolic diseases, improve their diagnosis, identify early disease markers and contribute to individualization of treatment and prevention plans.
Collapse
Affiliation(s)
- Ayya Keshet
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Lee Reicher
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- Lis Maternity and Women's Hospital, Tel Aviv Sourasky Medical Center, Tel Aviv University (affiliated with Sackler Faculty of Medicine), Tel Aviv, Israel
| | - Noam Bar
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
| |
Collapse
|
29
|
Bloem BR, Post E, Hall DA. An Apple a Day to Keep the Parkinson's Disease Doctor Away? Ann Neurol 2023; 93:681-685. [PMID: 36708048 DOI: 10.1002/ana.26612] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 01/26/2023] [Indexed: 01/29/2023]
Abstract
It is challenging to reliably assess the motor features of Parkinson's disease in real-time. This has motivated the search for new digital outcomes that can objectively and remotely measure the severity of parkinsonian motor impairments over an extended period of time. The United States Food and Drug Administration (FDA) has recently granted a 510(k) clearance to the Rune Labs Kinematics System, an ambulatory, smartwatch-based monitoring system to remotely track tremor and dyskinesias in persons with Parkinson's disease. The FDA clearance means that this new digital approach can be regarded as being safe for use in daily practice, with acceptable correlations to clinically based measures. However, the immediate implications for clinicians are limited, because it remains to be demonstrated whether the digital signals correlate well to clinically meaningful outcomes at patient level. The impact on research is also restricted for now, as more validation studies are needed before this new digital approach can be used as primary or secondary endpoint in clinical trials. ANN NEUROL 2023;93:681-685.
Collapse
Affiliation(s)
- Bastiaan R Bloem
- Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Erik Post
- Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Deborah A Hall
- Department of Neurological Sciences, Rush University, Chicago, IL, USA
| |
Collapse
|
30
|
Bender BF, Berry JA. Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population. ADVANCES IN GERIATRIC MEDICINE AND RESEARCH 2023; 5:e230002. [PMID: 37274061 PMCID: PMC10237513 DOI: 10.20900/agmr20230002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.
Collapse
Affiliation(s)
| | - Jasmine A. Berry
- Robotics Institute, University of Michigan, College of Engineering, Ann Arbor, MI 48109, USA
| |
Collapse
|
31
|
Oyama G, Burq M, Hatano T, Marks WJ, Kapur R, Fernandez J, Fujikawa K, Furusawa Y, Nakatome K, Rainaldi E, Chen C, Ho KC, Ogawa T, Kamo H, Oji Y, Takeshige-Amano H, Taniguchi D, Nakamura R, Sasaki F, Ueno S, Shiina K, Hattori A, Nishikawa N, Ishiguro M, Saiki S, Hayashi A, Motohashi M, Hattori N. Analytical and clinical validity of wearable, multi-sensor technology for assessment of motor function in patients with Parkinson's disease in Japan. Sci Rep 2023; 13:3600. [PMID: 36918552 PMCID: PMC10015076 DOI: 10.1038/s41598-023-29382-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/03/2023] [Indexed: 03/16/2023] Open
Abstract
Continuous, objective monitoring of motor signs and symptoms may help improve tracking of disease progression and treatment response in Parkinson's disease (PD). This study assessed the analytical and clinical validity of multi-sensor smartwatch measurements in hospitalized and home-based settings (96 patients with PD; mean wear time 19 h/day) using a twice-daily virtual motor examination (VME) at times representing medication OFF/ON states. Digital measurement performance was better during inpatient clinical assessments for composite V-scores than single-sensor-derived features for bradykinesia (Spearman |r|= 0.63, reliability = 0.72), tremor (|r|= 0.41, reliability = 0.65), and overall motor features (|r|= 0.70, reliability = 0.67). Composite levodopa effect sizes during hospitalization were 0.51-1.44 for clinical assessments and 0.56-1.37 for VMEs. Reliability of digital measurements during home-based VMEs was 0.62-0.80 for scores derived from weekly averages and 0.24-0.66 for daily measurements. These results show that unsupervised digital measurements of motor features with wrist-worn sensors are sensitive to medication state and are reliable in naturalistic settings.Trial Registration: Japan Pharmaceutical Information Center Clinical Trials Information (JAPIC-CTI): JapicCTI-194825; Registered June 25, 2019.
Collapse
Affiliation(s)
- Genko Oyama
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan.
| | - Maximilien Burq
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Taku Hatano
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - William J Marks
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Ritu Kapur
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Jovelle Fernandez
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Keita Fujikawa
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Yoshihiko Furusawa
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Keisuke Nakatome
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Erin Rainaldi
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Chen Chen
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - King Chung Ho
- Verily Life Sciences, 269 East Grand Avenue, South San Francisco, CA, USA
| | - Takashi Ogawa
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Hikaru Kamo
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Yutaka Oji
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Haruka Takeshige-Amano
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Daisuke Taniguchi
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Ryota Nakamura
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Fuyuko Sasaki
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Shinichi Ueno
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Kenta Shiina
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Anri Hattori
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Noriko Nishikawa
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Mayu Ishiguro
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Shinji Saiki
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| | - Ayako Hayashi
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Masatoshi Motohashi
- Takeda Pharmaceutical Company Limited, 2 Chome-1-1 Nihonbashihoncho, Chuo-Ku, Tokyo, 103-0023, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan
| |
Collapse
|
32
|
Hampel H, Gao P, Cummings J, Toschi N, Thompson PM, Hu Y, Cho M, Vergallo A. The foundation and architecture of precision medicine in neurology and psychiatry. Trends Neurosci 2023; 46:176-198. [PMID: 36642626 PMCID: PMC10720395 DOI: 10.1016/j.tins.2022.12.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/18/2022] [Accepted: 12/14/2022] [Indexed: 01/15/2023]
Abstract
Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.
Collapse
Affiliation(s)
- Harald Hampel
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA.
| | - Peng Gao
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy; Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yan Hu
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Min Cho
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Andrea Vergallo
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| |
Collapse
|
33
|
Bove R, Poole S, Cuneo R, Gupta S, Sabatino J, Harms M, Cooper T, Rowles W, Miller N, Gomez R, Lincoln R, McPolin K, Powers K, Santaniello A, Renschen A, Bevan CJ, Gelfand JM, Goodin DS, Guo CY, Romeo AR, Hauser SL, Campbell Cree BA. Remote Observational Research for Multiple Sclerosis: A Natural Experiment. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2023; 10:10/2/e200070. [PMID: 36585249 PMCID: PMC9808915 DOI: 10.1212/nxi.0000000000200070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 04/10/2022] [Indexed: 01/01/2023]
Abstract
BACKGROUND AND OBJECTIVES Prospective, deeply phenotyped research cohorts monitoring individuals with chronic neurologic conditions, such as multiple sclerosis (MS), depend on continued participant engagement. The COVID-19 pandemic restricted in-clinic research activities, threatening this longitudinal engagement, but also forced adoption of televideo-enabled care. This offered a natural experiment in which to analyze key dimensions of remote research: (1) comparison of remote vs in-clinic visit costs from multiple perspectives and (2) comparison of the remote with in-clinic measures in cross-sectional and longitudinal disability evaluations. METHODS Between March 2020 and December 2021, 207 MS cohort participants underwent hybrid in-clinic and virtual research visits; 96 contributed 100 "matched visits," that is, in-clinic (Neurostatus-Expanded Disability Status Scale [NS-EDSS]) and remote (televideo-enabled EDSS [tele-EDSS]; electronic patient-reported EDSS [ePR-EDSS]) evaluations. Clinical, demographic, and socioeconomic characteristics of participants were collected. RESULTS The costs of remote visits were lower than in-clinic visits for research investigators (facilities, personnel, parking, participant compensation) but also for participants (travel, caregiver time) and carbon footprint (p < 0.05 for each). Median cohort EDSS was similar between the 3 modalities (NS-EDSS: 2, tele-EDSS: 1.5, ePR-EDSS: 2, range 0.6.5); the remote evaluations were each noninferior to the NS-EDSS within ±0.5 EDSS point (TOST for noninferiority, p < 0.01 for each). Furthermore, year to year, the % of participants with worsening/stable/improved EDSS scores was similar, whether each annual evaluation used NS-EDSS or whether it switched from NS-EDSS to tele-EDSS. DISCUSSION Altogether, the current findings suggest that remote evaluations can reduce the costs of research participation for patients, while providing a reasonable evaluation of disability trajectory longitudinally. This could inform the design of remote research that is more inclusive of diverse participants.
Collapse
Affiliation(s)
- Riley Bove
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA.
| | - Shane Poole
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Richard Cuneo
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Sasha Gupta
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Joseph Sabatino
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Meagan Harms
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Tifffany Cooper
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - William Rowles
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Nicolette Miller
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Refujia Gomez
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Robin Lincoln
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Kira McPolin
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Kyra Powers
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Adam Santaniello
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Adam Renschen
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Carolyn J Bevan
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Jeffrey M Gelfand
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Douglas S Goodin
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Chu-Yueh Guo
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Andrew R Romeo
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Stephen L Hauser
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | | | | |
Collapse
|
34
|
Thawani SP, Minen MT, Stainman RS, Friedman S, Bhatt JM, Foo FYA, Torres DM, Weinberg HJ, Kim NH, Levitan V, Cardiel MI, Zakin E, Conway JM, Kurzweil AM, Hasanaj L, Galetta SL, Balcer LJ, Busis NA. Neurologists' Evaluations of Experience and Effectiveness of Teleneurology Encounters. Telemed J E Health 2023; 29:442-453. [PMID: 35834603 DOI: 10.1089/tmj.2021.0551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background and Objectives: To better understand neurologists' assessments of the experiences and effectiveness of teleneurology encounters. Methods: After completing an audio-video telehealth visit with verbally consenting patients, neurologists recorded their evaluations of the encounter. Data were analyzed using standard quantitative and qualitative techniques. Results: The study included unique encounters between 187 patients and 11 neurologists. The mean patient age was 49 ± 17.5 years. Two thirds of patients (66.8%, 125/187) were female. One third of patients (33.2%; 62) were new patients. The most common patient complaints were headache (69/187, 36.9%), focal and generalized numbness or tingling (21, 11.2%), memory difficulty (15, 8%), spine-related symptoms (12, 6.4%), and vertigo (11, 5.9%). Neurologists reported that they completed a virtual examination that provided enough information for medical decision-making in 94.9% of encounters (169/178, 9 missing responses). Fourteen of 25 examination elements important for medical decision-making could be performed sufficiently during virtual encounters. Examination assistance was needed for 16.4% (30/183) of patients, who were, on average, 17.3 years older than those who did not require assistance (62.9 years vs. 45.6 years, p = 0.0002). In 19.1% (34/178) of encounters, neurologists learned clinically relevant information from seeing patients in their homes. Neurologists' assessments of the effectiveness of encounters were not related to the presence (97.2%, 35/36 effective) or absence (95%, 134/141 effective) of technical difficulties (p = 0.5729) in 177 encounters (10 missing responses). Discussion: Neurologists reported that nearly 95% of teleneurology encounters were effective despite limitations of the virtual examination, occasional need for patient assistance, and technical difficulties.
Collapse
Affiliation(s)
- Sujata P Thawani
- Department of Neurology and NYU Langone Health, New York, New York, USA
| | - Mia T Minen
- Department of Neurology and NYU Langone Health, New York, New York, USA.,Department of Population Health, NYU Langone Health, New York, New York, USA
| | - Rebecca S Stainman
- Department of Neurology, Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Steven Friedman
- Department of Population Health, NYU Langone Health, New York, New York, USA
| | - Jaydeep M Bhatt
- Department of Neurology and NYU Langone Health, New York, New York, USA
| | - Farng-Yang A Foo
- Department of Neurology and NYU Langone Health, New York, New York, USA
| | - Daniel M Torres
- Department of Neurology and NYU Langone Health, New York, New York, USA
| | - Harold J Weinberg
- Department of Neurology and NYU Langone Health, New York, New York, USA
| | - Nina H Kim
- Department of Neurology and NYU Langone Health, New York, New York, USA
| | - Valeriya Levitan
- Department of Neurology and NYU Langone Health, New York, New York, USA
| | - Myrna I Cardiel
- Department of Neurology and NYU Langone Health, New York, New York, USA
| | - Elina Zakin
- Department of Neurology and NYU Langone Health, New York, New York, USA
| | - Jenna M Conway
- Department of Neurology and NYU Langone Health, New York, New York, USA
| | | | - Lisena Hasanaj
- Department of Neurology and NYU Langone Health, New York, New York, USA
| | - Steven L Galetta
- Department of Neurology and NYU Langone Health, New York, New York, USA.,Department of Ophthalmology, NYU Langone Health, New York, New York, USA
| | - Laura J Balcer
- Department of Neurology and NYU Langone Health, New York, New York, USA.,Department of Population Health, NYU Langone Health, New York, New York, USA.,Department of Ophthalmology, NYU Langone Health, New York, New York, USA
| | - Neil A Busis
- Department of Neurology and NYU Langone Health, New York, New York, USA
| |
Collapse
|
35
|
Sieberts SK, Borzymowski H, Guan Y, Huang Y, Matzner A, Page A, Bar-Gad I, Beaulieu-Jones B, El-Hanani Y, Goschenhofer J, Javidnia M, Keller MS, Li YC, Saqib M, Smith G, Stanescu A, Venuto CS, Zielinski R, Jayaraman A, Evers LJW, Foschini L, Mariakakis A, Pandey G, Shawen N, Synder P, Omberg L. Developing better digital health measures of Parkinson's disease using free living data and a crowdsourced data analysis challenge. PLOS DIGITAL HEALTH 2023; 2:e0000208. [PMID: 36976789 PMCID: PMC10047543 DOI: 10.1371/journal.pdig.0000208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 02/07/2023] [Indexed: 03/29/2023]
Abstract
One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson's disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians.
Collapse
Affiliation(s)
| | | | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yidi Huang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ayala Matzner
- Gonda Brain Research Center, Bar Ilan University, Ramat Gan, Israel
| | - Alex Page
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, United States of America
- Cardiology Division, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Izhar Bar-Gad
- Gonda Brain Research Center, Bar Ilan University, Ramat Gan, Israel
| | - Brett Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Yuval El-Hanani
- Gonda Brain Research Center, Bar Ilan University, Ramat Gan, Israel
| | | | - Monica Javidnia
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, United States of America
- Department of Neurology, University of Rochester, Rochester, New York, United States of America
| | - Mark S. Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yan-chak Li
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Mohammed Saqib
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Greta Smith
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, United States of America
- Department of Neurology, University of Rochester, Rochester, New York, United States of America
| | - Ana Stanescu
- Department of Computing and Mathematics, University of West Georgia, Carrollton, Georgia, United States of America
| | - Charles S. Venuto
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, United States of America
- Department of Neurology, University of Rochester, Rochester, New York, United States of America
| | - Robert Zielinski
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, United States of America
- Department of Neurology, University of Rochester, Rochester, New York, United States of America
| | | | - Arun Jayaraman
- Center for Rehabilitation Technologies & Outcomes Research, Shirley Ryan AbilityLab, Chicago, Illinois, United States of America
| | - Luc J. W. Evers
- Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
- Institute for Computing and Information Sciences, Radboud University, Nijmegen, the Netherlands
| | - Luca Foschini
- Evidation Health, Santa Barbara, California, United States of America
| | - Alex Mariakakis
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Nicholas Shawen
- Center for Rehabilitation Technologies & Outcomes Research, Shirley Ryan AbilityLab, Chicago, Illinois, United States of America
- Medical Scientist Training Program, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Phil Synder
- Sage Bionetworks, Seattle, Washington, United States of America
| | - Larsson Omberg
- Sage Bionetworks, Seattle, Washington, United States of America
| |
Collapse
|
36
|
A systematic review of local field potential physiomarkers in Parkinson's disease: from clinical correlations to adaptive deep brain stimulation algorithms. J Neurol 2023; 270:1162-1177. [PMID: 36209243 PMCID: PMC9886603 DOI: 10.1007/s00415-022-11388-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/16/2022] [Indexed: 02/03/2023]
Abstract
Deep brain stimulation (DBS) treatment has proven effective in suppressing symptoms of rigidity, bradykinesia, and tremor in Parkinson's disease. Still, patients may suffer from disabling fluctuations in motor and non-motor symptom severity during the day. Conventional DBS treatment consists of continuous stimulation but can potentially be further optimised by adapting stimulation settings to the presence or absence of symptoms through closed-loop control. This critically relies on the use of 'physiomarkers' extracted from (neuro)physiological signals. Ideal physiomarkers for adaptive DBS (aDBS) are indicative of symptom severity, detectable in every patient, and technically suitable for implementation. In the last decades, much effort has been put into the detection of local field potential (LFP) physiomarkers and in their use in clinical practice. We conducted a research synthesis of the correlations that have been reported between LFP signal features and one or more specific PD motor symptoms. Features based on the spectral beta band (~ 13 to 30 Hz) explained ~ 17% of individual variability in bradykinesia and rigidity symptom severity. Limitations of beta band oscillations as physiomarker are discussed, and strategies for further improvement of aDBS are explored.
Collapse
|
37
|
OYAMA GENKO, OGAWA MAYUKO, SEKIMOTO SATOKO, HATANO TAKU, HATTORI NOBUTAKA. A Narrative Review of Current Status and Future Perspective of Telemedicine for Parkinson's Disease, Dementia, and Intractable Neurological Diseases in Japan. JUNTENDO IJI ZASSHI = JUNTENDO MEDICAL JOURNAL 2023; 69:14-20. [PMID: 38854844 PMCID: PMC11153070 DOI: 10.14789/jmj.jmj22-0031-r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/20/2022] [Indexed: 06/11/2024]
Abstract
The coronavirus disease 2019 pandemic has uncovered several inherent problems in society. While the demand for telemedicine surged worldwide and some countries responded flexibly, in Japan, most telemedicine services were limited to telephone consultations, and full-fledged telemedicine did not become widespread. In addition, the digitalization process in both medicine and wider society lags behind some other nations. It is necessary to accelerate digital transformation in healthcare to build a sustainable society that is resilient to crises, such as new pandemics. In particular, as Japan is facing an issue of super-aged society, a sustainable care model for people with Parkinson's disease, dementia, and intractable neurological diseases should be established. Many neurodegenerative and intractable neurological diseases are progressive; as the disease progresses, patients could become difficult to visit specialists. Although online medical care has many advantages, it does not provide the same quality of information as face-to-face consultations. However, new technology can overcome the limitations of online medical care. As an evolutionary direction for telemedicine, three-dimensional telemedicine technologies are being developed, which enable online medical treatment to be delivered as if the patient was sharing the same space. Telemonitoring can enable the objective and continuous evaluation of patient information at home through the use of motion capture, wearable devices, and other devices. The advancement of digital transformation in medical care should be a game-changer in accumulating big data and analyzing it using artificial intelligence.
Collapse
Affiliation(s)
- GENKO OYAMA
- Corresponding author: Genko Oyama, Department of Neurology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan, TEL: +81-3-3813-3111 FAX: +81-3-5800-0547 E-mail:
| | | | | | | | | |
Collapse
|
38
|
Gerhalter T, Müller C, Maron E, Thielen M, Schätzl T, Mähler A, Schütte T, Boschmann M, Herzer R, Spuler S, Gazzerro E. "suMus," a novel digital system for arm movement metrics and muscle energy expenditure. Front Physiol 2023; 14:1057592. [PMID: 36776973 PMCID: PMC9909604 DOI: 10.3389/fphys.2023.1057592] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/11/2023] [Indexed: 01/27/2023] Open
Abstract
Objective: In the field of non-treatable muscular dystrophies, promising new gene and cell therapies are being developed and are entering clinical trials. Objective assessment of therapeutic effects on motor function is mandatory for economical and ethical reasons. Main shortcomings of existing measurements are discontinuous data collection in artificial settings as well as a major focus on walking, neglecting the importance of hand and arm movements for patients' independence. We aimed to create a digital tool to measure muscle function with an emphasis on upper limb motility. Methods: suMus provides a custom-made App running on smartwatches. Movement data are sent to the backend of a suMus web-based platform, from which they can be extracted as CSV data. Fifty patients with neuromuscular diseases assessed the pool of suMus activities in a first orientation phase. suMus performance was hence validated in four upper extremity exercises based on the feedback of the orientation phase. We monitored the arm metrics in a cohort of healthy volunteers using the suMus application, while completing each exercise at low frequency in a metabolic chamber. Collected movement data encompassed average acceleration, rotation rate as well as activity counts. Spearman rank tests correlated movement data with energy expenditure from the metabolic chamber. Results: Our novel application "suMus," sum of muscle activity, collects muscle movement data plus Patient-Related-Outcome-Measures, sends real-time feedback to patients and caregivers and provides, while ensuring data protection, a long-term follow-up of disease course. The application was well received from the patients during the orientation phase. In our pilot study, energy expenditure did not differ between overnight fasted and non-fasted participants. Acceleration ranged from 1.7 ± 0.7 to 3.2 ± 0.5 m/sec2 with rotation rates between 0.9 ± 0.5 and 2.0 ± 3.4 rad/sec. Acceleration and rotation rate as well as derived activity counts correlated with energy expenditure values measured in the metabolic chamber for one exercise (r = 0.58, p < 0.03). Conclusion: In the analysis of slow frequency movements of upper extremities, the integration of the suMus application with smartwatch sensors characterized motion parameters, thus supporting a use in clinical trial outcome measures. Alternative methodologies need to complement indirect calorimetry in validating accelerometer-derived energy expenditure data.
Collapse
Affiliation(s)
- Teresa Gerhalter
- Muscle Research Unit, Charité-Universitätsmedizin Berlin, Berlin, Germany,Experimental and Clinical Research Center, a joint Cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and the Charité-Universitätsmedizin Berlin, Berlin, Germany,Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | | | | | | | - Teresa Schätzl
- Muscle Research Unit, Charité-Universitätsmedizin Berlin, Berlin, Germany,Experimental and Clinical Research Center, a joint Cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and the Charité-Universitätsmedizin Berlin, Berlin, Germany,Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Anja Mähler
- Experimental and Clinical Research Center, a joint Cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and the Charité-Universitätsmedizin Berlin, Berlin, Germany,Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany,Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Till Schütte
- Experimental and Clinical Research Center, a joint Cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and the Charité-Universitätsmedizin Berlin, Berlin, Germany,Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany,Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany,Clinical Study Center (CSC), Berlin Institute of Health at Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Boschmann
- Experimental and Clinical Research Center, a joint Cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and the Charité-Universitätsmedizin Berlin, Berlin, Germany,Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany,Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | | | - Simone Spuler
- Muscle Research Unit, Charité-Universitätsmedizin Berlin, Berlin, Germany,Experimental and Clinical Research Center, a joint Cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and the Charité-Universitätsmedizin Berlin, Berlin, Germany,Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany,Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany,*Correspondence: Simone Spuler, ; Elisabetta Gazzerro,
| | - Elisabetta Gazzerro
- Muscle Research Unit, Charité-Universitätsmedizin Berlin, Berlin, Germany,Experimental and Clinical Research Center, a joint Cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and the Charité-Universitätsmedizin Berlin, Berlin, Germany,Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany,Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany,*Correspondence: Simone Spuler, ; Elisabetta Gazzerro,
| |
Collapse
|
39
|
Calvano A, Timmermann L, Loehrer PA, Oehrn CR, Weber I. Binaural acoustic stimulation in patients with Parkinson's disease. Front Neurol 2023; 14:1167006. [PMID: 37213909 PMCID: PMC10196363 DOI: 10.3389/fneur.2023.1167006] [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: 02/15/2023] [Accepted: 04/13/2023] [Indexed: 05/23/2023] Open
Abstract
Acoustic stimulation can improve motor symptoms in Parkinson's disease (PD) and might therefore represent a potential non-invasive treatment option. Scalp electroencephalography studies in healthy subjects indicate that specifically binaural beat stimulation (BBS) in the gamma frequency range is associated with synchronized cortical oscillations at 40 Hertz (Hz). Several studies suggest that oscillations in the gamma-frequency range (>30 Hz) serve a prokinetic function in PD. In this double-blind, randomized study, 25 PD patients were recruited. The study was conducted with (ON) and without dopaminergic medication (OFF). Each drug condition consisted of two phases (no stimulation and acoustic stimulation). The acoustic stimulation phase was divided into two blocks including BBS and conventional acoustic stimulation (CAS) as a control condition. For BBS, a modulated frequency of 35 Hz was used (left: 320 Hz; right: 355 Hz) and for CAS 340 Hz on both sides. We assessed effects on motor performance using Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and two validated commercially available portable devices (Kinesia ONE™ and Kinesia 360™) measuring motor symptoms such as dyskinesia, bradykinesia, and tremor. Repeated measures ANOVA revealed that BBS improved resting tremor on the side of the more affected limb in the OFF condition, as measured by wearables (F(2,48) = 3.61, p = 0.035). However, BBS did not exert a general positive effect on motor symptoms as assessed via MDS-UPDRS (F(2,48) = 1.00, p = 0.327). For CAS, we did not observe an improvement in specific symptoms but rather an overall beneficial effect on motor performance (MDS-UPDRS total score OFF medication: F(2,48) = 4.17, p = 0.021; wearable scores: F(2,48) = 2.46, p = 0.097). In this study, we found an improvement of resting tremor when applying BBS in the gamma frequency band OFF medication. Moreover, the positive effects of CAS underline the general positive potential for improvement of motor function by acoustically supported therapeutic approaches. However, more studies are needed to fully characterize the clinical relevance of BBS and to further optimize its ameliorating effects.
Collapse
Affiliation(s)
- Alexander Calvano
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
- *Correspondence: Alexander Calvano,
| | - Lars Timmermann
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-University Marburg, Marburg, Germany
| | - Philipp Alexander Loehrer
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-University Marburg, Marburg, Germany
| | - Carina Renate Oehrn
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-University Marburg, Marburg, Germany
| | - Immo Weber
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| |
Collapse
|
40
|
Wu Z, Gu H, Hong R, Xing Z, Zhang Z, Peng K, He Y, Xie L, Zhang J, Gao Y, Jin Y, Su X, Zhi H, Guan Q, Pan L, Jin L. Kinect-based objective evaluation of bradykinesia in patients with Parkinson's disease. Digit Health 2023; 9:20552076231176653. [PMID: 37223774 PMCID: PMC10201004 DOI: 10.1177/20552076231176653] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/02/2023] [Indexed: 05/25/2023] Open
Abstract
Objective To quantify bradykinesia in Parkinson's disease (PD) with a Kinect depth camera-based motion analysis system and to compare PD and healthy control (HC) subjects. Methods Fifty PD patients and twenty-five HCs were recruited. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was used to evaluate the motor symptoms of PD. Kinematic features of five bradykinesia-related motor tasks were collected using Kinect depth camera. Then, kinematic features were correlated with the clinical scales and compared between groups. Results Significant correlations were found between kinematic features and clinical scales (P < 0.05). Compared with HCs, PD patients exhibited a significant decrease in the frequency of finger tapping (P < 0.001), hand movement (P < 0.001), hand pronation-supination movements (P = 0.005), and leg agility (P = 0.003). Meanwhile, PD patients had a significant decrease in the speed of hand movements (P = 0.003) and toe tapping (P < 0.001) compared with HCs. Several kinematic features exhibited potential diagnostic value in distinguishing PD from HCs with area under the curve (AUC) ranging from 0.684-0.894 (P < 0.05). Furthermore, the combination of motor tasks exhibited the best diagnostic value with the highest AUC of 0.955 (95% CI = 0.913-0.997, P < 0.001). Conclusion The Kinect-based motion analysis system can be applied to evaluate bradykinesia in PD. Kinematic features can be used to differentiate PD patients from HCs and combining kinematic features from different motor tasks can significantly improve the diagnostic value.
Collapse
Affiliation(s)
- Zhuang Wu
- Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Persons’ Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
- Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Hongkai Gu
- Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Persons’ Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
- Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ronghua Hong
- Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Persons’ Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
- Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ziwen Xing
- Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Persons’ Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
- Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Zhuoyu Zhang
- Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Persons’ Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
- Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Kangwen Peng
- Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Persons’ Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
- Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yijing He
- Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Persons’ Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
- Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ludi Xie
- Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Persons’ Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
- Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jingxing Zhang
- Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yichen Gao
- IFLYTEK Suzhou Research Institute, Suzhou, China
| | - Yue Jin
- IFLYTEK Suzhou Research Institute, Suzhou, China
| | - Xiaoyun Su
- IFLYTEK Suzhou Research Institute, Suzhou, China
| | - Hongping Zhi
- IFLYTEK Suzhou Research Institute, Suzhou, China
| | - Qiang Guan
- Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lizhen Pan
- Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lingjing Jin
- Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Persons’ Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
- Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Collaborative Innovation Center for Brain Science, Tongji University, Shanghai, China
| |
Collapse
|
41
|
Gonzalez-Robles C, Weil RS, van Wamelen D, Bartlett M, Burnell M, Clarke CS, Hu MT, Huxford B, Jha A, Lambert C, Lawton M, Mills G, Noyce A, Piccini P, Pushparatnam K, Rochester L, Siu C, Williams-Gray CH, Zeissler ML, Zetterberg H, Carroll CB, Foltynie T, Schrag A. Outcome Measures for Disease-Modifying Trials in Parkinson's Disease: Consensus Paper by the EJS ACT-PD Multi-Arm Multi-Stage Trial Initiative. JOURNAL OF PARKINSON'S DISEASE 2023; 13:1011-1033. [PMID: 37545260 PMCID: PMC10578294 DOI: 10.3233/jpd-230051] [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] [Accepted: 06/23/2023] [Indexed: 08/08/2023]
Abstract
BACKGROUND Multi-arm, multi-stage (MAMS) platform trials can accelerate the identification of disease-modifying treatments for Parkinson's disease (PD) but there is no current consensus on the optimal outcome measures (OM) for this approach. OBJECTIVE To provide an up-to-date inventory of OM for disease-modifying PD trials, and a framework for future selection of OM for such trials. METHODS As part of the Edmond J Safra Accelerating Clinical Trials in Parkinson Disease (EJS ACT-PD) initiative, an expert group with Patient and Public Involvement and Engagement (PPIE) representatives' input reviewed and evaluated available evidence on OM for potential use in trials to delay progression of PD. Each OM was ranked based on aspects such as validity, sensitivity to change, participant burden and practicality for a multi-site trial. Review of evidence and expert opinion led to the present inventory. RESULTS An extensive inventory of OM was created, divided into: general, motor and non-motor scales, diaries and fluctuation questionnaires, cognitive, disability and health-related quality of life, capability, quantitative motor, wearable and digital, combined, resource use, imaging and wet biomarkers, and milestone-based. A framework for evaluation of OM is presented to update the inventory in the future. PPIE input highlighted the need for OM which reflect their experience of disease progression and are applicable to diverse populations and disease stages. CONCLUSION We present a range of OM, classified according to a transparent framework, to aid selection of OM for disease-modifying PD trials, whilst allowing for inclusion or re-classification of relevant OM as new evidence emerges.
Collapse
Affiliation(s)
| | | | | | | | - Matthew Burnell
- Medical Research Council Clinical Trials Unit at University College London, London, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
42
|
Kazi K, Ali SM, Selby DA, McBeth J, van der Veer S, Dixon WG. Examining the variability of multiple daily symptoms over time among individuals with multiple long-term conditions (MLTC-M/multimorbidity): An exploratory analysis of a longitudinal smartwatch feasibility study. JOURNAL OF MULTIMORBIDITY AND COMORBIDITY 2023; 13:26335565221150129. [PMID: 36698685 PMCID: PMC9869202 DOI: 10.1177/26335565221150129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 12/13/2023] [Indexed: 01/20/2023]
Abstract
Introduction People living with multiple long-term conditions (MLTC-M) (multimorbidity) experience a range of inter-related symptoms. These symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices, and then summarised to provide useful clinical insight. Aim We aimed to perform an exploratory analysis to summarise the extent and trajectory of multiple symptom ratings tracked via a smartwatch, and to investigate the relationship between these symptom ratings and demographic factors in people living with MLTC-M in a feasibility study. Methods 'Watch Your Steps' was a prospective observational feasibility study, administering multiple questions per day over a 90 day period. Adults with more than one clinician-diagnosed long-term condition rated seven core symptoms each day, plus up to eight additional symptoms personalised to their LTCs per day. Symptom ratings were summarised over the study period at the individual and group level. Symptom ratings were also plotted to describe day-to-day symptom trajectories for individuals. Results Fifty two participants submitted symptom ratings. Half were male and the majority had LTCs affecting three or more disease areas (N = 33, 64%). The symptom rated as most problematic was fatigue. Patients with increased comorbidity or female sex seemed to be associated with worse experiences of fatigue. Fatigue ratings were strongly correlated with pain and level of dysfunction. Conclusion In this study we have shown that it is possible to collect and descriptively analyse self reported symptom data in people living with MLTC-M, collected multiple times per day on a smartwatch, to gain insights that might support future clinical care and research.
Collapse
Affiliation(s)
- Khalid Kazi
- Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Syed Mustafa Ali
- Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - David A Selby
- Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - John McBeth
- Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester NHS Foundation Trust, Manchester, UK
| | - Sabine van der Veer
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - William G Dixon
- Northern Care Alliance NHS Foundation Trust, Salford, UK
- Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester NHS Foundation Trust, Manchester, UK
- William G Dixon, Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
| |
Collapse
|
43
|
Rastegari E, Ali H, Marmelat V. Detection of Parkinson's Disease Using Wrist Accelerometer Data and Passive Monitoring. SENSORS (BASEL, SWITZERLAND) 2022; 22:9122. [PMID: 36501823 PMCID: PMC9738242 DOI: 10.3390/s22239122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/11/2022] [Accepted: 11/18/2022] [Indexed: 06/17/2023]
Abstract
Parkinson's disease is a neurodegenerative disorder impacting patients' movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population's movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson's disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% ± 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection.
Collapse
Affiliation(s)
- Elham Rastegari
- Department of Business Intelligence and Analytics, Business College, Creighton University, Omaha, NE 68178, USA
| | - Hesham Ali
- Department of Biomedical Informatics, College of Information Systems and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
| | - Vivien Marmelat
- Department of Biomechanics, College of Education, Health and Human Sciences, University of Nebraska at Omaha, Omaha, NE 68182, USA
| |
Collapse
|
44
|
Khaliq F, Oberhauser J, Wakhloo D, Mahajani S. Decoding degeneration: the implementation of machine learning for clinical detection of neurodegenerative disorders. Neural Regen Res 2022; 18:1235-1242. [PMID: 36453399 PMCID: PMC9838151 DOI: 10.4103/1673-5374.355982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis, treatment, and tracking of complex conditions, including neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. While no definitive methods of diagnosis or treatment exist for either disease, researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers. Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment. However, such techniques require further development aimed at improving transparency, adaptability, and reproducibility. In this review, we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer's and Parkinson's diseases.
Collapse
Affiliation(s)
- Fariha Khaliq
- Department of Biomedical Engineering and Sciences (BMES), National University of Science and Technology, Islamabad, Pakistan,Correspondence to: Fariha Khaliq, ; Sameehan Mahajani, .
| | - Jane Oberhauser
- Department of Neuropathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Debia Wakhloo
- Department of Neuropathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sameehan Mahajani
- Department of Neuropathology, School of Medicine, Stanford University, Stanford, CA, USA,Correspondence to: Fariha Khaliq, ; Sameehan Mahajani, .
| |
Collapse
|
45
|
An integrated biometric voice and facial features for early detection of Parkinson's disease. NPJ Parkinsons Dis 2022; 8:145. [PMID: 36309501 PMCID: PMC9617232 DOI: 10.1038/s41531-022-00414-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/12/2022] [Indexed: 01/24/2023] Open
Abstract
Hypomimia and voice changes are soft signs preceding classical motor disability in patients with Parkinson's disease (PD). We aim to investigate whether an analysis of acoustic and facial expressions with machine-learning algorithms assist early identification of patients with PD. We recruited 371 participants, including a training cohort (112 PD patients during "on" phase, 111 controls) and a validation cohort (74 PD patients during "off" phase, 74 controls). All participants underwent a smartphone-based, simultaneous recording of voice and facial expressions, while reading an article. Nine different machine learning classifiers were applied. We observed that integrated facial and voice features could discriminate early-stage PD patients from controls with an area under the receiver operating characteristic (AUROC) diagnostic value of 0.85. In the validation cohort, the optimal diagnostic value (0.90) maintained. We concluded that integrated biometric features of voice and facial expressions could assist the identification of early-stage PD patients from aged controls.
Collapse
|
46
|
Bove F, Genovese D, Moro E. Developments in the mechanistic understanding and clinical application of deep brain stimulation for Parkinson's disease. Expert Rev Neurother 2022; 22:789-803. [PMID: 36228575 DOI: 10.1080/14737175.2022.2136030] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION. Deep brain stimulation (DBS) is a life-changing treatment for patients with Parkinson's disease (PD) and gives the unique opportunity to directly explore how basal ganglia work. Despite the rapid technological innovation of the last years, the untapped potential of DBS is still high. AREAS COVERED. This review summarizes the developments in the mechanistic understanding of DBS and the potential clinical applications of cutting-edge technological advances. Rather than a univocal local mechanism, DBS exerts its therapeutic effects through several multimodal mechanisms and involving both local and network-wide structures, although crucial questions remain unexplained. Nonetheless, new insights in mechanistic understanding of DBS in PD have provided solid bases for advances in preoperative selection phase, prediction of motor and non-motor outcomes, leads placement and postoperative stimulation programming. EXPERT OPINION. DBS has not only strong evidence of clinical effectiveness in PD treatment, but technological advancements are revamping its role of neuromodulation of brain circuits and key to better understanding PD pathophysiology. In the next few years, the worldwide use of new technologies in clinical practice will provide large data to elucidate their role and to expand their applications for PD patients, providing useful insights to personalize DBS treatment and follow-up.
Collapse
Affiliation(s)
- Francesco Bove
- Neurology Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Danilo Genovese
- Fresco Institute for Parkinson's and Movement Disorders, Department of Neurology, New York University School of Medicine, New York, New York, USA
| | - Elena Moro
- Grenoble Alpes University, CHU of Grenoble, Division of Neurology, Grenoble, France.,Grenoble Institute of Neurosciences, INSERM, U1216, Grenoble, France
| |
Collapse
|
47
|
Sui Y, Yu H, Zhang C, Chen Y, Jiang C, Li L. Deep brain-machine interfaces: sensing and modulating the human deep brain. Natl Sci Rev 2022; 9:nwac212. [PMID: 36644311 PMCID: PMC9834907 DOI: 10.1093/nsr/nwac212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/02/2022] [Accepted: 10/04/2022] [Indexed: 01/18/2023] Open
Abstract
Different from conventional brain-machine interfaces that focus more on decoding the cerebral cortex, deep brain-machine interfaces enable interactions between external machines and deep brain structures. They sense and modulate deep brain neural activities, aiming at function restoration, device control and therapeutic improvements. In this article, we provide an overview of multiple deep brain recording and stimulation techniques that can serve as deep brain-machine interfaces. We highlight two widely used interface technologies, namely deep brain stimulation and stereotactic electroencephalography, for technical trends, clinical applications and brain connectivity research. We discuss the potential to develop closed-loop deep brain-machine interfaces and achieve more effective and applicable systems for the treatment of neurological and psychiatric disorders.
Collapse
Affiliation(s)
- Yanan Sui
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | - Huiling Yu
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | - Chen Zhang
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | - Yue Chen
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | - Changqing Jiang
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | | |
Collapse
|
48
|
Xu Z, Shen B, Tang Y, Wu J, Wang J. Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:349-361. [PMID: 36939759 PMCID: PMC9590510 DOI: 10.1007/s43657-022-00051-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/12/2022] [Accepted: 03/28/2022] [Indexed: 11/27/2022]
Abstract
Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.
Collapse
Affiliation(s)
- Zhiheng Xu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Bo Shen
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Yilin Tang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jianjun Wu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jian Wang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| |
Collapse
|
49
|
Liu Y, Zhang G, Tarolli CG, Hristov R, Jensen-Roberts S, Waddell EM, Myers TL, Pawlik ME, Soto JM, Wilson RM, Yang Y, Nordahl T, Lizarraga KJ, Adams JL, Schneider RB, Kieburtz K, Ellis T, Dorsey ER, Katabi D. Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression, and medication response. Sci Transl Med 2022; 14:eadc9669. [PMID: 36130014 DOI: 10.1126/scitranslmed.adc9669] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Parkinson's disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people's bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients' daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.
Collapse
Affiliation(s)
- Yingcheng Liu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Guo Zhang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Christopher G Tarolli
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | | | - Stella Jensen-Roberts
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Emma M Waddell
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Taylor L Myers
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Meghan E Pawlik
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Julia M Soto
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Renee M Wilson
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Yuzhe Yang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Timothy Nordahl
- Department of Physical Therapy & Athletic Training, Center for Neurorehabilitation, Boston University College of Health and Rehabilitation: Sargent College, Boston, MA 02215, USA
| | - Karlo J Lizarraga
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Jamie L Adams
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Ruth B Schneider
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Karl Kieburtz
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Terry Ellis
- Department of Physical Therapy & Athletic Training, Center for Neurorehabilitation, Boston University College of Health and Rehabilitation: Sargent College, Boston, MA 02215, USA
| | - E Ray Dorsey
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Dina Katabi
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Emerald Innovations Inc., Cambridge, MA 02142, USA
| |
Collapse
|
50
|
Brand YE, Schwartz D, Gazit E, Buchman AS, Gilad-Bachrach R, Hausdorff JM. Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22187094. [PMID: 36146441 PMCID: PMC9502704 DOI: 10.3390/s22187094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/31/2022] [Accepted: 09/12/2022] [Indexed: 05/14/2023]
Abstract
Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly detection algorithm and compared its performance to four previously published gait detection algorithms. Multiday accelerometer recordings from a wrist-worn and lower-back sensor (i.e., the “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s disease (PD). The area under the receiver operator curve (AUC) and the area under the precision−recall curve (AUPRC) were used to evaluate the performance of the algorithms. The anomaly detection algorithm obtained AUCs of 0.80 and 0.74 for OAs and PD, respectively, but AUPRCs of 0.23 and 0.31 for OAs and PD, respectively. The best performing detection algorithm, a deep convolutional neural network (DCNN), exhibited high AUCs (i.e., 0.94 for OAs and 0.89 for PD) but lower AUPRCs (i.e., 0.66 for OAs and 0.60 for PD), indicating trade-offs between precision and recall. When choosing a classification threshold of 0.9 (i.e., opting for high precision) for the DCNN algorithm, strong correlations (r > 0.8) were observed between daily living walking time estimates based on the lower-back (reference) sensor and the wrist sensor. Further, gait quality measures were significantly different in OAs and PD compared to healthy adults. These results demonstrate that daily living gait can be quantified using a wrist-worn sensor.
Collapse
Affiliation(s)
- Yonatan E. Brand
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Dafna Schwartz
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
| | - Aron S. Buchman
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Ran Gilad-Bachrach
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- Edmond J. Safra Center for Bioinformatics, Tel-Aviv University, Tel Aviv 6997801, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Rush Alzheimer’s Disease Center and Department of Orthopedic Surgery, Rush University, Chicago, IL 60612, USA
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Correspondence:
| |
Collapse
|