RefCheck Maintenance Notice

On Monday, December 3, 2018, from 16:00-18:00 EST, RefCheck will be undergoing maintenance. RefCheck is the process where, during copyediting, all references are extracted from the manuscript file, parsed, matched against various databases (eg, PubMed and CrossRef), and automatically corrected. For more information on RefCheck, please visit our Knowledge Base.

Who will be affected?

Advertisement

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 06.03.15 in Vol 4, No 1 (2015): Jan-Mar

This paper is in the following e-collection/theme issue:

    Protocol

    Web-Based Telemonitoring and Delivery of Caregiver Support for Patients With Parkinson Disease After Deep Brain Stimulation: Protocol

    1Clinical Center for Neurostimulation, Neurotechnology, and Movement Disorders, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico, Milan, Italy

    2eHealthLAB, Department of Electronics, Information and Bioengineering, Polytechnic University of Milan, Milan, Italy

    3Clinical Center for Neurostimulation, Neurotechnology, and Movement Disorders, Fondazione IRCCS Ca\'Granda Ospedale Maggiore Policlinico, Milan, Italy

    4Section of Neurology, Department of Neurological, Neuropsychological, Morphological, and Motor Sciences, University of Verona, Verona, Italy

    5Clinical Center for Neurostimulation, Neurotechnology, and Movement Disorders, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico, Milan, Italy, Milan, Italy

    Corresponding Author:

    Sara Marceglia, MS, PhD

    Clinical Center for Neurostimulation, Neurotechnology, and Movement Disorders

    Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico

    via Francesco Sforza 35

    Milan, 20122

    Italy

    Phone: 39 025 032 0439

    Fax:39 025 503 3800

    Email:


    ABSTRACT

    Background: The increasing number of patients, the high costs of management, and the chronic progress of the disease that prevents patients from performing even simple daily activities make Parkinson disease (PD) a complex pathology with a high impact on society. In particular, patients implanted with deep brain stimulation (DBS) electrodes face a highly fragile stabilization period, requiring specific support at home. However, DBS patients are followed usually by untrained personnel (caregivers or family), without specific care pathways and supporting systems.

    Objective: This projects aims to (1) create a reference consensus guideline and a shared requirements set for the homecare and monitoring of DBS patients, (2) define a set of biomarkers that provides alarms to caregivers for continuous home monitoring, and (3) implement an information system architecture allowing communication between health care professionals and caregivers and improving the quality of care for DBS patients.

    Methods: The definitions of the consensus care pathway and of caregiver needs will be obtained by analyzing the current practices for patient follow-up through focus groups and structured interviews involving health care professionals, patients, and caregivers. The results of this analysis will be represented in a formal graphical model of the process of DBS patient care at home. To define the neurophysiological biomarkers to be used to raise alarms during the monitoring process, neurosignals will be acquired from DBS electrodes through a new experimental system that records while DBS is turned ON and transmits signals by radiofrequency. Motor, cognitive, and behavioral protocols will be used to study possible feedback/alarms to be provided by the system. Finally, a set of mobile apps to support the caregiver at home in managing and monitoring the patient will be developed and tested in the community of caregivers that participated in the focus groups. The set of developed apps will be connected to the already existing WebBioBank Web-based platform allowing health care professionals to manage patient electronic health records and neurophysiological signals. New modules in the WebBioBank platform will be implemented to allow integration and data exchange with mobile health apps.

    Results: The results of this project will provide a novel approach to long-term evaluation of patients with chronic, severe conditions in the homecare environment, based on caregiver empowerment and tailored applications developed according to consensus care pathways established by clinicians.

    Conclusions: The creation of a direct communication channel between health care professionals and caregivers can benefit large communities of patients and would represent a scalable experience in integrating data and information coming from a clinical setting to those in home monitoring.

    JMIR Res Protoc 2015;4(1):e30

    doi:10.2196/resprot.4044

    KEYWORDS

    Crowdfunding campaign to support this specific research

    We help JMIR researchers to raise funds to pursue their research and development aimed at tackling important health and technology challenges. If you would like to show your support for this author, please donate using the button below. The funds raised will directly benefit the corresponding author of this article (minus 8% admin fees). Your donations will help this author to continue publishing open access papers in JMIR journals. Donations of over $100 may also be acknowledged in future publications.

    keyboard with crowdfunding key instead of enter key

    Suggested contribution levels: $20/$50/$100



    Introduction

    Background

    Parkinson disease (PD) is a common neurodegenerative disorder affecting about 1% of the population over the age of 60 years [1]. PD is typically characterized by motor symptoms; however the clinical spectrum of the disease is more extensive, covering a wide range of nonmotor symptoms including cognitive and behavioral changes, sleep disorders, autonomic dysfunctions, sensory symptoms, and fatigue [2,3]. The surgical treatment of PD was reintroduced in the late 1990s with the advent of subthalamic deep brain stimulation (DBS) [4,5], and this is now an established treatment for PD patients [6-9].

    Follow-up studies in PD show that after DBS surgery, motor function and performance during daily living activities improve for up to 5 to 10 years, although the initial benefit in part progressively deteriorates [10] and patients still experience clinical fluctuations that prevent them from performing simple daily activities [11]. In addition, immediately after surgery` PD patients face a highly fragile stabilization period, in which parameters are set and patients and families get used to the new situation introduced with DBS.

    The long-term outcome of DBS depends on the development of unresponsive PD disturbances related to disease progression that should be properly monitored to allow early recognition and treatment [9].

    Long-term PD disability involves motor and nonmotor symptoms that worsen in time. Cognitive impairment and dementia have a higher prevalence in older patients [12,13]. Dementia and hallucinations predict nursing home placement and seem to double the mortality risk especially in older PD patients [14]. Another common, bothersome nonmotor symptom is the onset of lower urinary tract symptoms [15]. Up to 68% of PD patients fall every year, with approximately 50% falling repeatedly [16]. Cognitive behavioral education and exercise training were suggested to be effective in reducing the risk of falls [17,18]. Pain affects 29% to 85% of PD patients who experience various types of pain that may fluctuate with motor symptoms as “nonmotor fluctuations” [19]; the pain improves after DBS [20].

    PD progression often leads to hospitalization [21]. The most frequent causes for admission are falls, pneumonia, urinary tract infections, reduced mobility, psychiatric disorders, and mental status changes [22]. PD progression in DBS patients is monitored in scheduled follow-up clinical visits (once or twice a year) when the neurologist takes an instantaneous picture of the patient’s condition that cannot completely reflect the daily condition. At home, patients rely on a family caregiver, who is usually not trained to deal with PD progression and DBS. Often, the reference center is far from the patient's house.

    Research carried out in the last 15 years shows that recordings of neuronal activity (local field potentials [LFPs]) from the implanted electrodes provide information related to the patient's state [23-34]. LFPs correlate with motor and nonmotor PD symptoms and were recently proposed as a feedback variable for new DBS systems able to adapt stimulation parameters to the patient’s state [11]. Provided that the new generation of DBS stimulators will allow LFP recordings during stimulation [11], it is likely that the analysis of neuronal activity in DBS patients can be integrated into monitoring systems to provide feedback and alarms.

    These observations suggest that monitoring DBS patients more closely and continuously might be crucial to ensure a better quality of life. The use of telemedicine for monitoring PD patients has attracted a lot of attention [35-38], but no technology has been developed to support the continuous monitoring of DBS patients at home, including neurophysiological monitoring, together with an effective empowerment tool for caregivers.

    Objectives

    Consensus Guideline and Requirements Set

    The reference guideline will be developed by the multiprofessional team responsible for the patient’s care (neurologist, neurophysiologist, nurse, psychologist, physiotherapist, speech therapist) via modeling and defining the clinical care pathway of the DBS PD patient at home. In addition, the team will work with associates of the patient to establish a shared set of needs of the caregiver and patient community for the optimal management of patients with PD undergoing DBS at home.

    Neurophysiological Biomarkers

    Neurophysiological biomarkers provided by the neuronal signals recorded from deep brain electrodes can be used to directly monitor the patient's state at home. Motor, cognitive, and behavioral protocols will be used to study possible feedback/alarms to be provided by the system. These biomarkers will ground the future management of DBS PD patients at home, with the implanted stimulators being part of a system, already available as an experimental device, that will be able to record from DBS electrodes while stimulating.

    Information System Architecture

    The architecture of the information system will be created to meet the specific needs of caregivers at home by integrating the clinical care pathway of the patient in order to provide a caregiver support system connected to and sharing information with the electronic health record of the patient. The platform will be able to manage neurosignals analysis and provide alerts related to changes in biomarkers underlying changes in the patient's state. The architecture will hence be specific for home telemonitoring of DBS PD patients and empowerment of caregivers and include educational material, a direct communication channel with the specialist, and operative instructions for the recognition and management of simple alert symptoms.


    Methods

    Defining and Modeling the Process of Homecare Monitoring for DBS Patients

    To achieve the definition of the consensus care pathway for PD patients with DBS implant and establish a shared set of caregiver needs, we will analyze the current management practices by involving neurologists and psychologists (maximum 15) from the DBS Study Group of the Italian Neurological Society (Società Italiana di Neurologia) and organizing a focus group dedicated to homecare management of patients. The focus group will draft a consensus clinical care pathway that will be validated by a larger group of at least 30 PD experts.

    We will then prepare structured interviews for patients and caregivers to establish their main needs while at home, and we will interview patients and caregivers recruited through the Associazione Italiana Parkinson and directly through the Movement Disorders Ambulatory of both the Borgoroma Hospital and the Fondazione IRCCS Ca’Granda Ospedale Maggiore Policlinico. The expected number of patients and caregivers to be interviewed is 50.

    Finally, we will involve 10 to 15 patients, together with their caregivers and reference neurologists, in a focus group dedicated to the definition of the main needs and requirements for home monitoring support systems starting from the results of the interviews.

    After having completed the needs analysis for both health care professionals and patients and families, we will model through a graphical language (Unified Modeling Language [UML]) the care pathway of the PD patients with DBS at home, according to the clinical care pathway and the patient and caregiver needs.

    UML is a widely used visual language for specifying, visualizing, constructing, and documenting the artifacts of software systems, as well as for business modeling and other nonsoftware applications [39,40]. A UML model is composed of diagrams modeling the static and dynamic behaviors of the system/process. The UML modeling approach is based on the decomposition of a complex system into many different objects representing the subjects/objects related through hierarchical and functional relationships and specified through properties. The methods available to each object represent all the actions that the object exposes or receives. The methods available map all the existing interactions between objects in the system. This decomposed and simplified structure allows the introduction of a formal representation and supports the verification of its validity.

    Defining Neurophysiological Biomarkers

    To define neurophysiological biomarkers (Table 1) providing feedback on the patient’s state, we will record LFPs from 30 patients affected by PD and implanted with subthalamic nucleus DBS electrodes through a new experimental system that records LFPs while DBS is ON [41] and transmits signals by radiofrequency to a recording system (Figure 1). Recordings will take place 2 to 3 days after the surgery for electrode implantation and will record continuously for 3 hours. All of the experimental sessions will be videorecorded to allow further offline reassessment of the patient’s state by an experienced neurologist in order to detect motor fluctuations and other symptoms that may require feedback to the caregiver or to the reference neurologist.

    LFPs will be bipolarly recorded through DBS electrodes (Medtronic 3389) implanted in the subthalamic nucleus. The DBS electrode has four cylindrical contacts (0, 1, 2, and 3 beginning from the most caudal). Two contacts will be used for recording and one for DBS delivery. LFPs will be recorded through a new system (aDBS, Newronika srl) that allows LFP recording with DBS ON [41]; control of DBS parameters, manual and automatic, through ad hoc algorithms; and radiofrequency transmission of recorded signals.

    LFP analysis will be conducted offline with ad hoc programs stored on the WebBioBank system for clinical data collection. These Matlab-based programs allow signal preprocessing and analysis and were previously used for other LFP analyses [25,26,28,33].

    Because LFPs represent the activity of large populations of neurons, the analysis will be carried out in the frequency domain. Spectral analysis (power spectrum), cross-spectral analysis (coherence), parametric and nonparametric time-frequency analysis, and bispectral analysis (bicoherence) will be applied to extract biomarkers. Parametric and nonparametric tests will be used to evaluate the intrasubjective changes of LFP biomarkers in different conditions and during different tasks.

    Table 1. Protocols for defining neurophysiological biomarkers.
    View this table
    Figure 1. LFPs are recorded bipolarly from the DBS electrodes while the stimulation is turned ON through the aDBS device that also allows DBS parameter change and radiofrequency transmission of the recorded signals.
    View this figure

    Defining the Architecture of the Integrated Telecare System

    The implementation of the system architecture will be based on the extension of the already-existing WebBioBank system for the management of electronic health records (EHR) of PD patients with DBS electrode implants, integrated with signal analysis [42]. The system will be extended with a set of mobile apps to support the caregiver in managing and monitoring the patient at home.

    The architecture will consist of two modules, Care Pathway and Caregiver Support, which are both connected to the WebBioBank Web-based platform (Figure 2). The Care Pathway module will be a part of the WebBioBank EHR system and will be devoted to supporting the continuous monitoring of the disease progression and updating of home treatments, customized for each patient. The Caregiver Support module will be designed to provide the tools and information the patient and caregiver need for the proper implementation of the Care Pathway, according to the needs and requirements defined in the first phase of the project. A set of mobile apps will be made available to patients and caregivers to support symptom interpretation, emergency management, and routine daily activities and allow information exchange with the reference neurologist. A mobile device, such as a tablet, will be provided to patients who do not have a personal one.

    The system will be implemented and preliminary tested (alpha test) on the same patients involved in the focus groups previously described. System validation will be not part of this project.

    Figure 2. Expected architecture of the prototype system. The WebBioBank Web-based platform supports the information exchange between the Care Pathway module that defines the configuration of the homecare monitoring/treatments and the Caregiver Support module, a mobile app dedicated to the caregiver.
    View this figure

    Results

    The project is supported by preliminary observations showing that PD patient and caregiver associations are already active in asking for more effective communication with their care team and reliable education sources for the management of patients in homecare. Also, our previous studies show that neurophysiological signals from DBS electrodes provide information on PD motor and nonmotor symptoms [11,34], and we have already implemented a platform for the management and analysis of biopotentials recorded from DBS electrodes, integrated with WebBioBank [42].

    We expect at the end of the project to obtain a reference consensus guideline and a shared requirements set for the home monitoring and care of patients with PD treated with DBS that, combined with the set of biomarkers that will provide alarms/feedback to caregivers and health care professionals regarding the patient’s state, will constitute the implementation of continuous home monitoring for DBS patients. The implementation of a prototype information system architecture allowing communication between health care professionals and caregivers will support the improvement of the quality of care for PD patients treated with DBS.


    Discussion

    Patients with PD undergoing DBS face a highly fragile stabilization period and require specific support at home. At present, no system for DBS home monitoring is available. Our project, introducing effective direct and continuous monitoring of patients at home, would help not only to assess PD progression but also to make the patients and their families central actors in the care process.

    More specifically, the definition of consensus care pathways for patient home monitoring, the definition of neurophysiological biomarkers from signals recorded through DBS electrode, and the in-depth analysis of caregiver needs will ground the development of supporting tools and telemonitoring of DBS PD patients at home. Properly integrated with patient EHRs, these tools will improve the quality of life for DBS PD patients by supporting caregivers, optimizing patient continuous monitoring, and providing alerts to professionals and caregivers about disease worsening or reprogramming needs.

    The project results will contribute to the integration of the caregiver role in the care pathway of the patient through a direct communication channel between health care professionals and caregivers by means of an enhanced and easily accessible mobile app system for DBS patient home monitoring. In the project, we will also address the issue of ongoing support to caregivers through dedicated apps facilitating their education and interaction with local resources. Also, the definitions by clinicians of consensus care pathways and personalized care plans may produce better resource management for the national health care service and may improve, in terms of effectiveness and efficiency, the management of this costly neurodegenerative chronic disease.

    The experience in integrating data and information coming from a clinical setting to those in home monitoring can be scalable to other conditions involving patients with chronic diseases in the homecare environment, and tailored apps can be developed according to consensus care pathways established by clinicians.

    Conflicts of Interest

    SM, AP, SB, LR, and FC are founders and shareholders of Newronika Srl, a spin-off company of the Fondazione IRCCS Ca’Granda Ospedale Maggiore Policlinico and the University of Milan.

    References

    1. de Lau LM, Breteler MM. Epidemiology of Parkinson's disease. Lancet Neurol 2006 Jun;5(6):525-535. [CrossRef] [Medline]
    2. Chaudhuri KR, Healy DG, Schapira AH, National Institute for Clinical Excellence. Non-motor symptoms of Parkinson's disease: diagnosis and management. Lancet Neurol 2006 Mar;5(3):235-245. [CrossRef] [Medline]
    3. Schapira AH, Agid Y, Barone P, Jenner P, Lemke MR, Poewe W, et al. Perspectives on recent advances in the understanding and treatment of Parkinson's disease. Eur J Neurol 2009 Oct;16(10):1090-1099. [CrossRef] [Medline]
    4. Limousin P, Krack P, Pollak P, Benazzouz A, Ardouin C, Hoffmann D, et al. Electrical stimulation of the subthalamic nucleus in advanced Parkinson's disease. N Engl J Med 1998 Oct 15;339(16):1105-1111. [CrossRef] [Medline]
    5. Limousin P, Pollak P, Benazzouz A, Hoffmann D, Le Bas JF, Broussolle E, et al. Effect of parkinsonian signs and symptoms of bilateral subthalamic nucleus stimulation. Lancet 1995 Jan 14;345(8942):91-95. [Medline]
    6. Bronstein JM, Tagliati M, Alterman RL, Lozano AM, Volkmann J, Stefani A, et al. Deep brain stimulation for Parkinson disease: an expert consensus and review of key issues. Arch Neurol 2011 Feb;68(2):165. [CrossRef] [Medline]
    7. Kleiner-Fisman G, Herzog J, Fisman DN, Tamma F, Lyons KE, Pahwa R, et al. Subthalamic nucleus deep brain stimulation: summary and meta-analysis of outcomes. Mov Disord 2006 Jun;21 Suppl 14:S290-S304. [CrossRef] [Medline]
    8. Deuschl G, Schade-Brittinger C, Krack P, Volkmann J, Schäfer H, Bötzel K, German Parkinson Study Group‚ Neurostimulation Section. A randomized trial of deep-brain stimulation for Parkinson's disease. N Engl J Med 2006 Aug 31;355(9):896-908. [CrossRef] [Medline]
    9. Moro E, Poon YY, Lozano AM, Saint-Cyr JA, Lang AE. Subthalamic nucleus stimulation: improvements in outcome with reprogramming. Arch Neurol 2006 Sep;63(9):1266-1272. [CrossRef] [Medline]
    10. Castrioto A, Lozano AM, Poon YY, Lang AE, Fallis M, Moro E. Ten-year outcome of subthalamic stimulation in Parkinson disease: a blinded evaluation. Arch Neurol 2011 Dec;68(12):1550-1556. [CrossRef] [Medline]
    11. Priori A, Foffani G, Rossi L, Marceglia S. Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations. Exp Neurol 2013 Jul;245:77-86. [CrossRef] [Medline]
    12. Hely MA, Reid WG, Adena MA, Halliday GM, Morris JG. The Sydney multicenter study of Parkinson's disease: the inevitability of dementia at 20 years. Mov Disord 2008 Apr 30;23(6):837-844. [CrossRef] [Medline]
    13. Kehagia AA, Barker RA, Robbins TW. Neuropsychological and clinical heterogeneity of cognitive impairment and dementia in patients with Parkinson's disease. Lancet Neurol 2010 Dec;9(12):1200-1213. [CrossRef] [Medline]
    14. Forsaa EB, Larsen JP, Wentzel-Larsen T, Alves G. What predicts mortality in Parkinson disease?: a prospective population-based long-term study. Neurology 2010 Oct 5;75(14):1270-1276. [CrossRef] [Medline]
    15. Winge K, Nielsen KK, Stimpel H, Lokkegaard A, Jensen SR, Werdelin L. Lower urinary tract symptoms and bladder control in advanced Parkinson's disease: effects of deep brain stimulation in the subthalamic nucleus. Mov Disord 2007 Jan 15;22(2):220-225. [CrossRef] [Medline]
    16. Wood BH, Bilclough JA, Bowron A, Walker RW. Incidence and prediction of falls in Parkinson's disease: a prospective multidisciplinary study. J Neurol Neurosurg Psychiatry 2002 Jun;72(6):721-725 [FREE Full text] [Medline]
    17. Mak MK, Pang MY, Mok V. Gait difficulty, postural instability, and muscle weakness are associated with fear of falling in people with Parkinson's disease. Parkinsons Dis 2012;2012:901721 [FREE Full text] [CrossRef] [Medline]
    18. Nilsson MH, Rehncrona S, Jarnlo GB. Fear of falling and falls in people with Parkinson's disease treated with deep brain stimulation in the subthalamic nuclei. Acta Neurol Scand 2011 Jun;123(6):424-429. [CrossRef] [Medline]
    19. Kim HJ, Jeon BS, Paek SH. Effect of deep brain stimulation on pain in Parkinson disease. J Neurol Sci 2011 Nov 15;310(1-2):251-255. [CrossRef] [Medline]
    20. Kim HJ, Jeon BS, Lee JY, Paek SH, Kim DG. The benefit of subthalamic deep brain stimulation for pain in Parkinson disease: a 2-year follow-up study. Neurosurgery 2012 Jan;70(1):18-23; discussion 23. [CrossRef] [Medline]
    21. Zibetti M, Merola A, Rizzi L, Ricchi V, Angrisano S, Azzaro C, et al. Beyond nine years of continuous subthalamic nucleus deep brain stimulation in Parkinson's disease. Mov Disord 2011 Nov;26(13):2327-2334. [CrossRef] [Medline]
    22. Chou KL, Zamudio J, Schmidt P, Price CC, Parashos SA, Bloem BR, et al. Hospitalization in Parkinson disease: a survey of National Parkinson Foundation Centers. Parkinsonism Relat Disord 2011 Jul;17(6):440-445 [FREE Full text] [CrossRef] [Medline]
    23. Giannicola G, Rosa M, Marceglia S, Scelzo E, Rossi L, Servello D, et al. The effects of levodopa and deep brain stimulation on subthalamic local field low-frequency oscillations in Parkinson's disease. Neurosignals 2013;21(1-2):89-98. [CrossRef] [Medline]
    24. Marceglia S, Rossi L, Foffani G, Bianchi A, Cerutti S, Priori A. Basal ganglia local field potentials: applications in the development of new deep brain stimulation devices for movement disorders. Expert Rev Med Devices 2007 Sep;4(5):605-614. [CrossRef] [Medline]
    25. Foffani G, Bianchi AM, Baselli G, Priori A. Movement-related frequency modulation of beta oscillatory activity in the human subthalamic nucleus. J Physiol 2005 Oct 15;568(Pt 2):699-711 [FREE Full text] [CrossRef] [Medline]
    26. Foffani G, Priori A, Egidi M, Rampini P, Tamma F, Caputo E, et al. 300-Hz subthalamic oscillations in Parkinson's disease. Brain 2003 Oct;126(Pt 10):2153-2163 [FREE Full text] [CrossRef] [Medline]
    27. Foffani G, Ardolino G, Rampini P, Tamma F, Caputo E, Egidi M, et al. Physiological recordings from electrodes implanted in the basal ganglia for deep brain stimulation in Parkinson's disease. the relevance of fast subthalamic rhythms. Acta Neurochir Suppl 2005;93:97-99. [Medline]
    28. Fumagalli M, Giannicola G, Rosa M, Marceglia S, Lucchiari C, Mrakic-Sposta S, et al. Conflict-dependent dynamic of subthalamic nucleus oscillations during moral decisions. Soc Neurosci 2011;6(3):243-256. [CrossRef] [Medline]
    29. Giannicola G, Marceglia S, Rossi L, Mrakic-Sposta S, Rampini P, Tamma F, et al. The effects of levodopa and ongoing deep brain stimulation on subthalamic beta oscillations in Parkinson's disease. Exp Neurol 2010 Nov;226(1):120-127. [CrossRef] [Medline]
    30. Rosa M, Giannicola G, Marceglia S, Fumagalli M, Barbieri S, Priori A. Neurophysiology of deep brain stimulation. Int Rev Neurobiol 2012;107:23-55. [CrossRef] [Medline]
    31. Brown P, Williams D. Basal ganglia local field potential activity: character and functional significance in the human. Clin Neurophysiol 2005 Nov;116(11):2510-2519. [CrossRef] [Medline]
    32. Little S, Brown P. The functional role of beta oscillations in Parkinson's disease. Parkinsonism Relat Disord 2014 Jan;20 Suppl 1:S44-S48. [CrossRef] [Medline]
    33. Priori A, Foffani G, Pesenti A, Tamma F, Bianchi AM, Pellegrini M, et al. Rhythm-specific pharmacological modulation of subthalamic activity in Parkinson's disease. Exp Neurol 2004 Oct;189(2):369-379. [CrossRef] [Medline]
    34. Marceglia S, Fumagalli M, Priori A. What neurophysiological recordings tell us about cognitive and behavioral functions of the human subthalamic nucleus. Expert Rev Neurother 2011 Jan;11(1):139-149. [CrossRef] [Medline]
    35. Biglan KM, Voss TS, Deuel LM, Miller D, Eason S, Fagnano M, et al. Telemedicine for the care of nursing home residents with Parkinson's disease. Mov Disord 2009 May 15;24(7):1073-1076. [CrossRef] [Medline]
    36. Chen BR, Patel S, Buckley T, Rednic R, McClure DJ, Shih L, et al. A web-based system for home monitoring of patients with Parkinson's disease using wearable sensors. IEEE Trans Biomed Eng 2011 Mar;58(3):831-836. [CrossRef] [Medline]
    37. Cunningham L, Mason S, Nugent C, Moore G, Finlay D, Craig D. Home-based monitoring and assessment of Parkinson's disease. IEEE Trans Inf Technol Biomed 2011 Jan;15(1):47-53. [CrossRef] [Medline]
    38. Goetz CG, Stebbins GT, Wolff D, DeLeeuw W, Bronte-Stewart H, Elble R, et al. Testing objective measures of motor impairment in early Parkinson's disease: Feasibility study of an at-home testing device. Mov Disord 2009 Mar 15;24(4):551-556 [FREE Full text] [CrossRef] [Medline]
    39. Eriksson HE. Business modeling with UML: business patterns at work. New York: Wiley; 2000.
    40. Booch G. The unified modeling language user guide. Upper Saddle River, NJ: Addison-Wesley; 2005.
    41. Rossi L, Foffani G, Marceglia S, Bracchi F, Barbieri S, Priori A. An electronic device for artefact suppression in human local field potential recordings during deep brain stimulation. J Neural Eng 2007 Jun;4(2):96-106. [CrossRef] [Medline]
    42. Rossi E, Rosa M, Rossi L, Priori A, Marceglia S. WebBioBank: A new platform for integrating clinical forms and shared neurosignal analyses to support multi-centre studies in Parkinson's Disease. J Biomed Inform 2014 Dec;52:92-104. [CrossRef] [Medline]


    Abbreviations

    PD: Parkinson disease
    DBS: deep brain stimulation
    LFP: local field potential
    UML: Unified Modeling Language
    EHR: electronic health record


    Edited by G Eysenbach; This is a non-peer-reviewed article. submitted 15.11.14; accepted 25.11.14; published 06.03.15

    ©Sara Marceglia, Elena Rossi, Manuela Rosa, Filippo Cogiamanian, Lorenzo Rossi, Laura Bertolasi, Alberto Vogrig, Francesco Pinciroli, Sergio Barbieri, Alberto Priori. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 06.03.2015.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.