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      New Sensor and Wearable Technologies to Aid in the Diagnosis and Treatment Monitoring of Parkinson's Disease

      1 , 2 , 1 , 3 , 1 , 4 , 1 , 4 , 5
      Annual Review of Biomedical Engineering
      Annual Reviews

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          Abstract

          Parkinson's disease (PD) is a degenerative disorder of the brain characterized by the impairment of the nigrostriatal system. This impairment leads to specific motor manifestations (i.e., bradykinesia, tremor, and rigidity) that are assessed through clinical examination, scales, and patient-reported outcomes. New sensor-based and wearable technologies are progressively revolutionizing PD care by objectively measuring these manifestations and improving PD diagnosis and treatment monitoring. However, their use is still limited in clinical practice, perhaps because of the absence of external validation and standards for their continuous use at home. In the near future, these systems will progressively complement traditional tools and revolutionize the way we diagnose and monitor patients with PD.

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          Most cited references94

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          Alzheimer's disease and Parkinson's disease.

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            The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service.

            We have reviewed the clinical and pathological diagnoses of 143 cases of parkinsonism seen by neurologists associated with the movement disorders service at The National Hospital for Neurology and Neurosurgery in London who came to neuropathological examination at the United Kingdom Parkinson's Disease Society Brain Research Centre, over a 10-year period between 1990 and the end of 1999. Seventy-three (47 male, 26 female) cases were diagnosed as having idiopathic Parkinson's disease (IPD) and 70 (42 male, 28 female) as having another parkinsonian syndrome. The positive predictive value of the clinical diagnosis for the whole group was 85.3%, with 122 cases correctly clinically diagnosed. The positive predictive value of the clinical diagnosis of IPD was extremely high, at 98.6% (72 out of 73), while for the other parkinsonian syndromes it was 71.4% (50 out of 70). The positive predictive values of a clinical diagnosis of multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) were 85.7 (30 out of 35) and 80% (16 out of 20), respectively. The sensitivity for IPD was 91.1%, due to seven false-negative cases, with 72 of the 79 pathologically established cases being diagnosed in life. For MSA, the sensitivity was 88.2% (30 out of 34), and for PSP it was 84.2% (16 out of 19). The diagnostic accuracy for IPD, MSA and PSP was higher than most previous prospective clinicopathological series and studies using the retrospective application of clinical diagnostic criteria. The seven false-negative cases of IPD suggest a broader clinical picture of disease than previously thought acceptable. This study implies that neurologists with particular expertise in the field of movement disorders may be using a method of pattern recognition for diagnosis which goes beyond that inherent in any formal set of diagnostic criteria.
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              Technology in Parkinson's disease: Challenges and opportunities.

              The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society.
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                Author and article information

                Journal
                Annual Review of Biomedical Engineering
                Annu. Rev. Biomed. Eng.
                Annual Reviews
                1523-9829
                1545-4274
                June 04 2019
                June 04 2019
                : 21
                : 1
                : 111-143
                Affiliations
                [1 ]HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU–San Pablo, 28938 Móstoles, Madrid, Spain;, , ,
                [2 ]Department of Anatomy, Histology and Neuroscience, School of Medicine, Universidad Autónoma de Madrid, 28029 Madrid, Spain
                [3 ]Hospital Nacional de Parapléjicos, Servicio de Salud de Castilla La Mancha, 45071 Toledo, Spain
                [4 ]Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, 28031 Madrid, Spain
                [5 ]Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
                Article
                10.1146/annurev-bioeng-062117-121036
                31167102
                ee2e8fc0-9727-45d0-b757-d4583fd137f4
                © 2019
                History

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