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Langeskov-Christensen M, Franzén E, Grøndahl Hvid L, Dalgas U. Exercise as medicine in Parkinson's disease. J Neurol Neurosurg Psychiatry 2024:jnnp-2023-332974. [PMID: 38418216 DOI: 10.1136/jnnp-2023-332974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 02/02/2024] [Indexed: 03/01/2024]
Abstract
Parkinson's disease (PD) is an incurable and progressive neurological disorder leading to deleterious motor and non-motor consequences. Presently, no pharmacological agents can prevent PD evolution or progression, while pharmacological symptomatic treatments have limited effects in certain domains and cause side effects. Identification of interventions that prevent, slow, halt or mitigate the disease is therefore pivotal. Exercise is safe and represents a cornerstone in PD rehabilitation, but exercise may have even more fundamental benefits that could change clinical practice. In PD, the existing knowledge base supports exercise as (1) a protective lifestyle factor preventing the disease (ie, primary prevention), (2) a potential disease-modifying therapy (ie, secondary prevention) and (3) an effective symptomatic treatment (ie, tertiary prevention). Based on current evidence, a paradigm shift is proposed, stating that exercise should be individually prescribed as medicine to persons with PD at an early disease stage, alongside conventional medical treatment.
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Affiliation(s)
- Martin Langeskov-Christensen
- Exercise Biology, Department of Public Health, Aarhus University, Aarhus, Denmark
- Department of Neurology, Viborg Regional Hospital, Viborg, Denmark
| | - Erika Franzén
- Department of Neurobiology, Care Sciences and Society, Division of Physiotherapy, Karolinska Institutet, Stockholm, Sweden
- Department of Physical Therapy, Karolinska University Hospital, Stockholm, Sweden
| | - Lars Grøndahl Hvid
- Exercise Biology, Department of Public Health, Aarhus University, Aarhus, Denmark
- The Danish MS Hospitals, Ry and Haslev, Denmark
| | - Ulrik Dalgas
- Exercise Biology, Department of Public Health, Aarhus University, Aarhus, Denmark
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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.
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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
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Bibbo D, De Marchis C, Schmid M, Ranaldi S. Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review. Physiol Meas 2023; 44:12TR01. [PMID: 38061062 DOI: 10.1088/1361-6579/ad133b] [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: 06/19/2023] [Accepted: 12/07/2023] [Indexed: 12/27/2023]
Abstract
This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.
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Affiliation(s)
- Daniele Bibbo
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy
| | | | - Maurizio Schmid
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy
| | - Simone Ranaldi
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy
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Zhao A, Cui E, Leroux A, Lindquist MA, Crainiceanu CM. Evaluating the prediction performance of objective physical activity measures for incident Parkinson's disease in the UK Biobank. J Neurol 2023; 270:5913-5923. [PMID: 37612539 DOI: 10.1007/s00415-023-11939-0] [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: 07/06/2023] [Revised: 08/08/2023] [Accepted: 08/11/2023] [Indexed: 08/25/2023]
Abstract
BACKGROUND Parkinson's disease (PD) is the fastest-growing neurological condition with over 10 million cases worldwide. While age and sex are known predictors of incident PD, there is a need to identify other predictors. This study compares the prediction performance of accelerometry-derived physical activity (PA) measures and traditional risk factors for incident PD in the UK Biobank. METHODS The study population consisted of 92,352 UK Biobank participants without PD at baseline (43.8% male, median age 63 years with interquartile range 43-69). 245 participants were diagnosed with PD by April 1, 2021 (586,604 person-years of follow-up). The incident PD prediction performances of 10 traditional predictors and 8 objective PA measures were compared using single- and multi-variable Cox models. Prediction performance was assessed using a novel, stable statistic: the repeated cross-validated concordance (rcvC). Sensitivity analyses were conducted where PD cases diagnosed within the first six months, one year, and two years were deleted. RESULTS Single-predictor Cox regression models indicated that all PA measures were statistically significant (p-values < 0.0001). The highest-performing individual predictors were total acceleration (TA) (rcvC = 0.813) among PA measures, and age (rcvC = 0.757) among traditional predictors. The two-step forward-selection process produced a model containing age, sex, and TA (rcvC = 0.851). Adding TA to the model increased the rcvC by 9.8% (p-value < 0.0001). Results were largely unchanged in sensitivity analyses. CONCLUSIONS Objective PA summaries have better single-predictor model performance than known risk factors and increase the prediction performance substantially when added to models with age and sex.
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Affiliation(s)
- Angela Zhao
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
| | - Erjia Cui
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
- Division of Biostatistics, University of Minnesota Twin Cities, Minneapolis, USA
| | - Andrew Leroux
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
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Canonico M, Desimoni F, Ferrero A, Grassi PA, Irwin C, Campani D, Dal Molin A, Panella M, Magistrelli L. Gait Monitoring and Analysis: A Mathematical Approach. SENSORS (BASEL, SWITZERLAND) 2023; 23:7743. [PMID: 37765801 PMCID: PMC10536663 DOI: 10.3390/s23187743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/29/2023] [Accepted: 09/02/2023] [Indexed: 09/29/2023]
Abstract
Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson's, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes. In this paper, we studied older adults affected by chronic diseases and/or Parkinson's disease by monitoring their gait due to wearable devices that can accurately detect a person's movements. In our study, about 50 people were involved in the trial (20 with Parkinson's disease and 30 people with chronic diseases) who have worn our device for at least 6 months. During the experimentation, each device collected 25 samples from the accelerometer sensor for each second. By analyzing those data, we propose a metric for the "gait quality" based on the measure of entropy obtained by applying the Fourier transform.
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Affiliation(s)
- Massimo Canonico
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, Italy; (F.D.); (A.F.); (P.A.G.); (C.I.)
| | - Francesco Desimoni
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, Italy; (F.D.); (A.F.); (P.A.G.); (C.I.)
| | - Alberto Ferrero
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, Italy; (F.D.); (A.F.); (P.A.G.); (C.I.)
| | - Pietro Antonio Grassi
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, Italy; (F.D.); (A.F.); (P.A.G.); (C.I.)
| | - Christopher Irwin
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, 15121 Alessandria, Italy; (F.D.); (A.F.); (P.A.G.); (C.I.)
| | - Daiana Campani
- Department of Translational Medicine, Università del Piemonte Orientale, 28100 Novara, Italy; (D.C.); (A.D.M.); (M.P.); (L.M.)
| | - Alberto Dal Molin
- Department of Translational Medicine, Università del Piemonte Orientale, 28100 Novara, Italy; (D.C.); (A.D.M.); (M.P.); (L.M.)
| | - Massimiliano Panella
- Department of Translational Medicine, Università del Piemonte Orientale, 28100 Novara, Italy; (D.C.); (A.D.M.); (M.P.); (L.M.)
| | - Luca Magistrelli
- Department of Translational Medicine, Università del Piemonte Orientale, 28100 Novara, Italy; (D.C.); (A.D.M.); (M.P.); (L.M.)
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Brink-Kjær A, Wickramaratne SD, Parekh A, During EH. Detection and Characterization of Walking Bouts Using a Single Wrist-Worn Accelerometer in Free-living Conditions. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.01.23293509. [PMID: 37577642 PMCID: PMC10418291 DOI: 10.1101/2023.08.01.23293509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Detection and characterization of abnormalities of movement are important to develop a method for detecting early signs of Parkinson's disease (PD). Most of the current research in detection of characteristic reduction of movements due to PD, known as parkinsonism, requires using a set of invasive sensors in a clinical or controlled environment. Actigraphy has been widely used in medical research as a non-invasive data acquisition method in free-living conditions for long periods of time. The proposed algorithm uses triaxial accelerometer data obtained through actigraphy to detect walking bouts at least 10 seconds long and characterize them using cadence and arm swing. Accurate detection of walking periods is the first step toward the characterization of movement based on gait abnormalities. The algorithm was based on a Walking Score (WS) derived using the value of the auto-correlation function (ACF) for the Resultant acceleration vector. The algorithm achieved a precision of 0.90, recall of 0.77, and F1 score of 0.83 compared to the expert scoring for walking bout detection. We additionally described a method to measure arm swing amplitude.
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Schalkamp AK, Peall KJ, Harrison NA, Sandor C. Wearable movement-tracking data identify Parkinson's disease years before clinical diagnosis. Nat Med 2023; 29:2048-2056. [PMID: 37400639 DOI: 10.1038/s41591-023-02440-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/05/2023] [Indexed: 07/05/2023]
Abstract
Parkinson's disease is a progressive neurodegenerative movement disorder with a long latent phase and currently no disease-modifying treatments. Reliable predictive biomarkers that could transform efforts to develop neuroprotective treatments remain to be identified. Using UK Biobank, we investigated the predictive value of accelerometry in identifying prodromal Parkinson's disease in the general population and compared this digital biomarker with models based on genetics, lifestyle, blood biochemistry or prodromal symptoms data. Machine learning models trained using accelerometry data achieved better test performance in distinguishing both clinically diagnosed Parkinson's disease (n = 153) (area under precision recall curve (AUPRC) 0.14 ± 0.04) and prodromal Parkinson's disease (n = 113) up to 7 years pre-diagnosis (AUPRC 0.07 ± 0.03) from the general population (n = 33,009) compared with all other modalities tested (genetics: AUPRC = 0.01 ± 0.00, P = 2.2 × 10-3; lifestyle: AUPRC = 0.03 ± 0.04, P = 2.5 × 10-3; blood biochemistry: AUPRC = 0.01 ± 0.00, P = 4.1 × 10-3; prodromal signs: AUPRC = 0.01 ± 0.00, P = 3.6 × 10-3). Accelerometry is a potentially important, low-cost screening tool for determining people at risk of developing Parkinson's disease and identifying participants for clinical trials of neuroprotective treatments.
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Affiliation(s)
- Ann-Kathrin Schalkamp
- Division of Psychological Medicine and Clinical Neuroscience, UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Kathryn J Peall
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, UK
| | - Neil A Harrison
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, UK
| | - Cynthia Sandor
- Division of Psychological Medicine and Clinical Neuroscience, UK Dementia Research Institute, Cardiff University, Cardiff, UK.
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ZhuParris A, de Goede AA, Yocarini IE, Kraaij W, Groeneveld GJ, Doll RJ. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115243. [PMID: 37299969 DOI: 10.3390/s23115243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. OBJECTIVE This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. METHODS This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. RESULTS This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. CONCLUSION mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.
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Affiliation(s)
- Ahnjili ZhuParris
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Annika A de Goede
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
| | - Iris E Yocarini
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Wessel Kraaij
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- The Netherlands Organisation for Applied Scientific Research (TNO), Anna van Buerenplein 1, 2595 DA, Den Haag, The Netherlands
| | - Geert Jan Groeneveld
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Robert Jan Doll
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
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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.
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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
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Williamson JR, Kim J, Halford E, Smalt CJ, Rao HM. Using Body-worn Accelerometers to Detect Physiological Changes During Periods of Blast Overpressure Exposure. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:926-932. [PMID: 36086014 DOI: 10.1109/embc48229.2022.9871620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Repetitive exposure to non-concussive blast expo-sure may result in sub-clinical neurological symptoms. These changes may be reflected in the neural control gait and balance. In this study, we collected body-worn accelerometry data on individuals who were exposed to repetitive blast overpressures as part of their occupation. Accelerometry features were gener-ated within periods of low-movement and gait. These features were the eigenvalues of high-dimensional correlation matrices, which were constructed with time-delay embedding at multiple delay scales. When focusing on the gait windows, there were significant correlations of the changes in features with the cumulative dose of blast exposure. When focusing on the low-movement frames, the correlation with exposure were lower than that of the gait frames and statistically insignificant. In a cross-validated model, the overpressure exposure was predicted from gait features alone. The model was statistically significant and yielded an RMSE of 1.27 dB. With continued development, the model may be used to assess the physiological effects of repetitive blast exposure and guide training procedures to minimize impact on the individual.
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Burq M, Rainaldi E, Ho KC, Chen C, Bloem BR, Evers LJW, Helmich RC, Myers L, Marks WJ, Kapur R. Virtual exam for Parkinson's disease enables frequent and reliable remote measurements of motor function. NPJ Digit Med 2022; 5:65. [PMID: 35606508 PMCID: PMC9126938 DOI: 10.1038/s41746-022-00607-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 04/28/2022] [Indexed: 12/30/2022] Open
Abstract
Sensor-based remote monitoring could help better track Parkinson's disease (PD) progression, and measure patients' response to putative disease-modifying therapeutic interventions. To be useful, the remotely-collected measurements should be valid, reliable, and sensitive to change, and people with PD must engage with the technology. We developed a smartwatch-based active assessment that enables unsupervised measurement of motor signs of PD. Participants with early-stage PD (N = 388, 64% men, average age 63) wore a smartwatch for a median of 390 days. Participants performed unsupervised motor tasks both in-clinic (once) and remotely (twice weekly for one year). Dropout rate was 5.4%. Median wear-time was 21.1 h/day, and 59% of per-protocol remote assessments were completed. Analytical validation was established for in-clinic measurements, which showed moderate-to-strong correlations with consensus MDS-UPDRS Part III ratings for rest tremor (⍴ = 0.70), bradykinesia (⍴ = -0.62), and gait (⍴ = -0.46). Test-retest reliability of remote measurements, aggregated monthly, was good-to-excellent (ICC = 0.75-0.96). Remote measurements were sensitive to the known effects of dopaminergic medication (on vs off Cohen's d = 0.19-0.54). Of note, in-clinic assessments often did not reflect the patients' typical status at home. This demonstrates the feasibility of smartwatch-based unsupervised active tests, and establishes the analytical validity of associated digital measurements. Weekly measurements provide a real-life distribution of disease severity, as it fluctuates longitudinally. Sensitivity to medication-induced change and improved reliability imply that these methods could help reduce sample sizes needed to demonstrate a response to therapeutic interventions or disease progression.
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Affiliation(s)
- Maximilien Burq
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | - Erin Rainaldi
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | - King Chung Ho
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | - Chen Chen
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | - Bastiaan R. Bloem
- grid.5590.90000000122931605Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Luc J. W. Evers
- grid.5590.90000000122931605Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands ,grid.5590.90000000122931605Radboud University, Institute for Computing and Information Sciences, Nijmegen, the Netherlands
| | - Rick C. Helmich
- grid.5590.90000000122931605Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Lance Myers
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | | | - Ritu Kapur
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
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Giannakopoulou KM, Roussaki I, Demestichas K. Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:1799. [PMID: 35270944 PMCID: PMC8915040 DOI: 10.3390/s22051799] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/17/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
Parkinson's disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients' quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson's disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson's disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson's disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.
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Affiliation(s)
- Konstantina-Maria Giannakopoulou
- School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (K.-M.G.); (K.D.)
- Institute of Communication and Computer Systems, 10682 Athens, Greece
| | - Ioanna Roussaki
- School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (K.-M.G.); (K.D.)
- Institute of Communication and Computer Systems, 10682 Athens, Greece
| | - Konstantinos Demestichas
- School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (K.-M.G.); (K.D.)
- Institute of Communication and Computer Systems, 10682 Athens, Greece
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13
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Xie Y, Lu L, Gao F, He SJ, Zhao HJ, Fang Y, Yang JM, An Y, Ye ZW, Dong Z. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Curr Med Sci 2021; 41:1123-1133. [PMID: 34950987 PMCID: PMC8702375 DOI: 10.1007/s11596-021-2485-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/03/2021] [Indexed: 12/19/2022]
Abstract
Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.
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Affiliation(s)
- Yi Xie
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Lin Lu
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Fei Gao
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Shuang-Jiang He
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Hui-Juan Zhao
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Ying Fang
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Jia-Ming Yang
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Ying An
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Wuhan Fourth Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430032, China
| | - Zhe-Wei Ye
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Zhe Dong
- School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
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14
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Clark S, Lomax N, Morris M, Pontin F, Birkin M. Clustering Accelerometer Activity Patterns from the UK Biobank Cohort. SENSORS (BASEL, SWITZERLAND) 2021; 21:8220. [PMID: 34960314 PMCID: PMC8709415 DOI: 10.3390/s21248220] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/19/2021] [Accepted: 12/03/2021] [Indexed: 11/17/2022]
Abstract
Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we report the development of a clustering utilising the whole activity profile. This was achieved using the robust clustering technique of k-medoids applied to an extensive data set of over 90,000 activity profiles, collected as part of the UK Biobank study. We identified nine distinct activity profiles in these data, which captured both the pattern of activity throughout a week and the intensity of the activity: "Active 9 to 5", "Active", "Morning Movers", "Get up and Active", "Live for the Weekend", "Moderates", "Leisurely 9 to 5", "Sedate" and "Inactive". These patterns are differentiated by sociodemographic, socioeconomic, and health and circadian rhythm data collected by UK Biobank. The utility of these findings are that they sit alongside existing summary measures of physical activity to provide a way to typify distinct activity patterns that may help to explain other health and morbidity outcomes, e.g., BMI or COVID-19. This research will be returned to the UK Biobank for other researchers to use.
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Affiliation(s)
- Stephen Clark
- Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds LS2 9JT, UK; (N.L.); (F.P.); (M.B.)
| | - Nik Lomax
- Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds LS2 9JT, UK; (N.L.); (F.P.); (M.B.)
| | - Michelle Morris
- Leeds Institute for Data Analytics and School of Medicine, University of Leeds, Leeds LS2 9JT, UK;
| | - Francesca Pontin
- Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds LS2 9JT, UK; (N.L.); (F.P.); (M.B.)
| | - Mark Birkin
- Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds LS2 9JT, UK; (N.L.); (F.P.); (M.B.)
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15
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Williamson JR, Sturim D, Vian T, Lacirignola J, Shenk TE, Yuditskaya S, Rao HM, Talavage TM, Heaton KJ, Quatieri TF. Using Dynamics of Eye Movements, Speech Articulation and Brain Activity to Predict and Track mTBI Screening Outcomes. Front Neurol 2021; 12:665338. [PMID: 34295299 PMCID: PMC8289895 DOI: 10.3389/fneur.2021.665338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 06/09/2021] [Indexed: 11/24/2022] Open
Abstract
Repeated subconcussive blows to the head during sports or other contact activities may have a cumulative and long lasting effect on cognitive functioning. Unobtrusive measurement and tracking of cognitive functioning is needed to enable preventative interventions for people at elevated risk of concussive injury. The focus of the present study is to investigate the potential for using passive measurements of fine motor movements (smooth pursuit eye tracking and read speech) and resting state brain activity (measured using fMRI) to complement existing diagnostic tools, such as the Immediate Post-concussion Assessment and Cognitive Testing (ImPACT), that are used for this purpose. Thirty-one high school American football and soccer athletes were tracked through the course of a sports season. Hypotheses were that (1) measures of complexity of fine motor coordination and of resting state brain activity are predictive of cognitive functioning measured by the ImPACT test, and (2) within-subject changes in these measures over the course of a sports season are predictive of changes in ImPACT scores. The first principal component of the six ImPACT composite scores was used as a latent factor that represents cognitive functioning. This latent factor was positively correlated with four of the ImPACT composites: verbal memory, visual memory, visual motor speed and reaction speed. Strong correlations, ranging between r = 0.26 and r = 0.49, were found between this latent factor and complexity features derived from each sensor modality. Based on a regression model, the complexity features were combined across sensor modalities and used to predict the latent factor on out-of-sample subjects. The predictions correlated with the true latent factor with r = 0.71. Within-subject changes over time were predicted with r = 0.34. These results indicate the potential to predict cognitive performance from passive monitoring of fine motor movements and brain activity, offering initial support for future application in detection of performance deficits associated with subconcussive events.
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Affiliation(s)
- James R Williamson
- Human Health and Performance Systems, MIT Lincoln Laboratory, Lexington, MA, United States
| | - Doug Sturim
- Human Health and Performance Systems, MIT Lincoln Laboratory, Lexington, MA, United States
| | - Trina Vian
- Counter-WMD Systems, MIT Lincoln Laboratory, Lexington, MA, United States
| | - Joseph Lacirignola
- Counter-WMD Systems, MIT Lincoln Laboratory, Lexington, MA, United States
| | - Trey E Shenk
- Advanced RF Techniques & Systems, MIT Lincoln Laboratory, Lexington, MA, United States
| | - Sophia Yuditskaya
- Human Health and Performance Systems, MIT Lincoln Laboratory, Lexington, MA, United States
| | - Hrishikesh M Rao
- Human Health and Performance Systems, MIT Lincoln Laboratory, Lexington, MA, United States
| | - Thomas M Talavage
- Electrical and Computer Engineering/Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Kristin J Heaton
- Military Performance Division, U.S. Army Research Institute of Environmental Medicine, Natick, MA, United States
| | - Thomas F Quatieri
- Human Health and Performance Systems, MIT Lincoln Laboratory, Lexington, MA, United States
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