476 research outputs found

    Track Myself:a smartphone-based tool for monitoring Parkinson’s disease

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    Abstract. Parkinson disease (PD) is a fast-spreading neurological disorder that affects millions of people worldwide, it hinders its patients from performing daily activities with ease. Its symptoms may vary within hours and progress differently for each patient, and usually assessed clinically every six months. It requires customized treatment plan for each patient and demands adherence of patients to complex medication regimens. The goal of this thesis is to design, implement, and test a mobile app named “Track Myself” that can help people with Parkinson’s disease (PwP) resolve these issues. The app has two components that help PwP assess their symptoms level regularly, the first component is an accelerometer-based game that detects the patient’s hand movement and calculate a score for its accuracy, the second component is a self-report symptoms survey filled by the patient every day to rate their severity level. A medication journal is implemented in the app for the patients to log their medication intakes regularly, which are prescribed by their doctors using the app as well, this help keep track of the medication history and calculate the patient’s medication adherence. The app also contains a dashboard made of three charts, representing the medication time-adherence, symptom surveys, and game scores of the patient. The purpose of this dashboard is to help the doctors form relationships between the data in the charts and determine the best future treatment plan. The app was tested for two weeks by ten healthy participants, they were asked to act in the persona of a PD patient and perform certain tasks, where information about the disease and experiences of actual patients were provided for these participants. A questionnaire was sent to the participants after the study, it consists of open-ended questions, rating statements, as well as a validated mobile health app usability questionnaire (MAUQ). The participants rated the app as easy to use for PwP in most features with mean score of 6.04/7 and perceived the app as very useful in helping PwP with mean score of 6.18/7

    Smart Wearable Device for Reduction of Parkinson’s Disease Hand-Tremor

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    Parkinson\u27s disease is a neurodegenerative disorder that affects over 10 million people worldwide (Health Unlocked, 2017). People diagnosed with Parkinson\u27s Disease can experience tremors, muscular rigidity and slowness of movement. Tremor is the most common symptom and external agents like stress and anxiety can make it worse, which may cause complications to complete simple day-to-day tasks. Therefore Bio Protech proposes the development of a smart wearable device for reduction of the hand-tremors based on medical evidence that by applying vibration to the wrist may result in a reduction of the involuntary tremor. The device imitates the shape of a wristwatch and the vibration is supplied by motors placed around the wrist. The users will be given the possibility to regulate the frequency according to their needs using a mobile application connected via Bluetooth

    Designing socially acceptable mHealth technologies for Parkinson's disease self-management

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    Mobile health (mHealth) technologies for Parkinson’s disease management have developed quickly in recent years. Research in this area typically focuses on evaluation of the accuracy and reliability of the technology, often to the exclusion of social factors and patient perspectives. This qualitative systematic review aimed to investigate the barriers to and facilitators of use mHealth technologies for disease self-management from the perspective of People with Parkinson's (PwP). Findings revealed that technological, as well as social, and financial factors are key considerations for mHealth design, to ensure its acceptability, and long-term use by PwP. This study proposes that a co-design approach could contribute to the design and development of mHealth that are socially acceptable to PwP, and enable their successful long-term use in the context of daily life.Mobile health (mHealth) technologies for Parkinson’s disease management have developed quickly in recent years. Research in this area typically focuses on evaluation of the accuracy and reliability of the technology, often to the exclusion of social factors and patient perspectives. This qualitative systematic review aimed to investigate the barriers to and facilitators of use mHealth technologies for disease self-management from the perspective of People with Parkinson's (PwP). Findings revealed that technological, as well as social, and financial factors are key considerations for mHealth design, to ensure its acceptability, and long-term use by PwP. This study proposes that a co-design approach could contribute to the design and development of mHealth that are socially acceptable to PwP, and enable their successful long-term use in the context of daily life

    Clinical Decision Support Systems with Game-based Environments, Monitoring Symptoms of Parkinson’s Disease with Exergames

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    Parkinson’s Disease (PD) is a malady caused by progressive neuronal degeneration, deriving in several physical and cognitive symptoms that worsen with time. Like many other chronic diseases, it requires constant monitoring to perform medication and therapeutic adjustments. This is due to the significant variability in PD symptomatology and progress between patients. At the moment, this monitoring requires substantial participation from caregivers and numerous clinic visits. Personal diaries and questionnaires are used as data sources for medication and therapeutic adjustments. The subjectivity in these data sources leads to suboptimal clinical decisions. Therefore, more objective data sources are required to better monitor the progress of individual PD patients. A potential contribution towards more objective monitoring of PD is clinical decision support systems. These systems employ sensors and classification techniques to provide caregivers with objective information for their decision-making. This leads to more objective assessments of patient improvement or deterioration, resulting in better adjusted medication and therapeutic plans. Hereby, the need to encourage patients to actively and regularly provide data for remote monitoring remains a significant challenge. To address this challenge, the goal of this thesis is to combine clinical decision support systems with game-based environments. More specifically, serious games in the form of exergames, active video games that involve physical exercise, shall be used to deliver objective data for PD monitoring and therapy. Exergames increase engagement while combining physical and cognitive tasks. This combination, known as dual-tasking, has been proven to improve rehabilitation outcomes in PD: recent randomized clinical trials on exergame-based rehabilitation in PD show improvements in clinical outcomes that are equal or superior to those of traditional rehabilitation. In this thesis, we present an exergame-based clinical decision support system model to monitor symptoms of PD. This model provides both objective information on PD symptoms and an engaging environment for the patients. The model is elaborated, prototypically implemented and validated in the context of two of the most prominent symptoms of PD: (1) balance and gait, as well as (2) hand tremor and slowness of movement (bradykinesia). While balance and gait affections increase the risk of falling, hand tremors and bradykinesia affect hand dexterity. We employ Wii Balance Boards and Leap Motion sensors, and digitalize aspects of current clinical standards used to assess PD symptoms. In addition, we present two dual-tasking exergames: PDDanceCity for balance and gait, and PDPuzzleTable for tremor and bradykinesia. We evaluate the capability of our system for assessing the risk of falling and the severity of tremor in comparison with clinical standards. We also explore the statistical significance and effect size of the data we collect from PD patients and healthy controls. We demonstrate that the presented approach can predict an increased risk of falling and estimate tremor severity. Also, the target population shows a good acceptance of PDDanceCity and PDPuzzleTable. In summary, our results indicate a clear feasibility to implement this system for PD. Nevertheless, long-term randomized clinical trials are required to evaluate the potential of PDDanceCity and PDPuzzleTable for physical and cognitive rehabilitation effects

    A smartphone-based system for detecting hand tremors in unconstrained environments

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    The detection of tremors can be crucial for the early diagnosis and proper treatment of some disorders such as Parkinson’s disease. A smartphone-based applica- tion has been developed for detecting hand tremors. This application runs in background and distinguishes hand tremors from common daily activities. This application can facilitate the continuous monitoring of patients or the early detection of this symptom. The evaluation analyzes 1770 accelerometer samples with cross-validation for assessing the ability of the system for processing unknown data, obtaining a sensitivity of 95.8 % and a specificity of 99.5 %. It also analyzes continuous data for some volun- teers for several days, which corroborated its high performance

    Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KAVELI) : Protocol for an Observational Case-Control Study

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    Background: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. Objective: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. Methods: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. Results: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. Conclusions: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders.Peer reviewe

    Human activity detection based on mobile devices

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    Aquesta tesi se centra en la detecció d'activitat humana a partir de dispositius mòbils i portàtils. Escollim Hexiwear com el nostre dispositiu portàtil per recollir les dades de l'activitat humana diària, com ara l'acceleració de tres eixos, l'orientació de tres eixos, la velocitat angular i la posició de tres eixos. Aquest projecte consisteix en el desenvolupament d'una aplicació per a telèfon intel·ligent per a l'usuari en l'anàlisi de dades, la visualització de dades i la generació de resultats. L'objectiu és construir un prototip obert i modular que pugui servir d'exemple o plantilla per al desenvolupament d'altres projectes. L'aplicació està desenvolupada amb JAVA per Android Studio. L'aplicació permet a l'usuari connectar-se amb el dispositiu portàtil i reconèixer la seva activitat diària. Per a l'algorisme de classificació de l'activitat diària, hem utilitzat dos mètodes diferents, el primer és mitjançant l'establiment de diferents llindars, el segon és mitjançant l'aprenentatge automàtic. L'aplicació es va provar i els resultats van ser satisfactoris, ja que l'aplicació generada va funcionar correctament. Malgrat les òbvies limitacions, la feina feta és un punt de partida per a desenvolupaments futurs。Esta tesis se centra en la detección de actividad humana basada en dispositivos móviles y portátiles. Elegimos Hexiwear como nuestro dispositivo portátil para recopilar los datos de la actividad humana diaria, como la aceleración de tres ejes, la orientación de tres ejes, la velocidad angular de tres ejes y la posición. Este proyecto implica la creación de una aplicación de teléfono para usuarios de análisis de datos, visualización de datos y generación de resultados. El objetivo es construir un prototipo abierto y modular que pueda servir como ejemplo o plantilla para el desarrollo de otros proyectos. La aplicación está desarrollada usando JAVA por Android Studio. La aplicación permite al usuario conectarse con el dispositivo portátil y reconocer su actividad diaria. Para el algoritmo de clasificación de la actividad diaria, usamos dos métodos diferentes, el primero es establecer umbrales diferentes, el segundo es usar el aprendizaje automático. La aplicación fue probada y los resultados fueron satisfactorios, ya que la aplicación generada funcionó correctamente. A pesar de las limitaciones evidentes, el trabajo realizado es un punto de partida para futuros desarrollos.  This thesis focuses on human activity detection based on mobile and wearable devices. We choose Hexiwear as our wearable device to collect the human daily activity data, like tri-axis acceleration, tri-axis orientation, tri-axis angular velocity and position. This project consists in the development of a smartphone application for the user in data analysis, data visualization and generates results. The objective is to build an open and modular prototype that can serve as an example or template for the development of other projects. The application is developed using JAVA by Android Studio. The application allows the user to connect with the wearable device, and recognize their daily activity. For the daily activity classify algorithm, we used two different methods, the first one is by set different thresholds, the second is by using the machine learning. The application was tested and the results were satisfactory, as the generated application worked properly. Despite the obvious limitations, the work done is a starting point for future developments

    Mobile clinical decision support systems and applications: a literature and commercial review

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    The final publication is available at Springer via http://dx.doi.org/10.1007/s10916-013-0004-y[EN] Background: The latest advances in eHealth and mHealth have propitiated the rapidly creation and expansion of mobile applications for health care. One of these types of applications are the clinical decision support systems, which nowadays are being implemented in mobile apps to facilitate the access to health care professionals in their daily clinical decisions. Objective: The aim of this paper is twofold. Firstly, to make a review of the current systems available in the literature and in commercial stores. Secondly, to analyze a sample of applications in order to obtain some conclusions and recommendations. Methods: Two reviews have been done: a literature review on Scopus, IEEE Xplore, Web of Knowledge and PubMed and a commercial review on Google play and the App Store. Five applications from each review have been selected to develop an in-depth analysis and to obtain more information about the mobile clinical decision support systems. Results: 92 relevant papers and 192 commercial apps were found. 44 papers were focused only on mobile clinical decision support systems. 171 apps were available on Google play and 21 on the App Store. The apps are designed for general medicine and 37 different specialties, with some features common in all of them despite of the different medical fields objective. Conclusions: The number of mobile clinical decision support applications and their inclusion in clinical practices has risen in the last years. However, developers must be careful with their interface or the easiness of use, which can impoverish the experience of the users.This research has been partially supported by Ministerio de Economía y Competitividad, Spain. This research has been partially supported by the ICT-248765 EU-FP7 Project. This research has been partially supported by the IPT-2011-1126-900000 project under the INNPACTO 2011 program, Ministerio de Ciencia e Innovación.Martínez Pérez, B.; De La Torre Diez, I.; López Coronado, M.; Sainz De Abajo, B.; Robles Viejo, M.; García Gómez, JM. (2014). Mobile clinical decision support systems and applications: a literature and commercial review. Journal of Medical Systems. 38(1):1-10. https://doi.org/10.1007/s10916-013-0004-yS110381Van De Belt, T. H., Engelen, L. J., Berben, S. A., and Schoonhoven, L., Definition of Health 2.0 and Medicine 2.0: A systematic review. J Med Internet Res 2010:12(2), 2012.Oh, H., Rizo, C., Enkin, M., and Jadad, A., What is eHealth (3): A systematic review of published definitions. J Med Internet Res 7(1):1, 2005. PMID: 15829471.World Health Organization (2011) mHealth: New horizons for health through mobile technologies: Based on the findings of the second global survey on eHealth (Global Observatory for eHealth Series, Volume 3). World Health Organization. 2011. ISBN: 9789241564250Lin, C., Mobile telemedicine: A survey study. J Med Syst April 36(2):511–520, 2012.El Khaddar, M.A., Harroud, H., Boulmalf, M., Elkoutbi, M., Habbani, A., Emerging wireless technologies in e-health Trends, challenges, and framework design issues. 2012 International Conference on Multimedia Computing and Systems (ICMCS). 440–445, 2012.Luanrattana, R., Win, K. T., Fulcher, J., and Iverson, D., Mobile technology use in medical education. J Med Syst 36(1):113–122, 2012.Yang, S. C., Mobile applications and 4 G wireless networks: A framework for analysis. Campus-Wide Information Systems 29(5):344–357, 2012.Kumar, B., Singh, S.P., Mohan, A., Emerging mobile communication technologies for health. 2010 International Conference on Computer and Communication Technology, ICCCT-2010; Allahabad; pp. 828–832, 2010.Yan, H., Huo, H., Xu, Y., and Gidlund, M., Wireless sensor network based E-health system—implementation and experimental results. IEEE Transactions on Consumer Electronics 56(4):2288–2295, 2010.IDC (2013) Press release: Strong demand for smartphones and heated vendor competition characterize the worldwide mobile phone market at the end of 2012. http://www.idc.com/getdoc.jsp?containerId=prUS23916413#.UVBKiRdhWCn . Accessed 11 September 2013.IDC (2012) IDC Raises its worldwide tablet forecast on continued strong demand and forthcoming new product launches. http://www.idc.com/getdoc.jsp?containerId=prUS23696912#.US9x86JhWCl . Accessed 11 September 2013.International Data Corporation (2013) Android and iOS combine for 91.1 % of the worldwide smartphone OS market in 4Q12 and 87.6 % for the year. http://www.idc.com/getdoc.jsp?containerId=prUS23946013 . Accessed 11 September 2013.Jones, C., (2013) Apple and Google continue to gain US Smartphone market share. Forbes. http://www.forbes.com/sites/chuckjones/2013/01/04/apple-and-google-continue-to-gain-us-smartphone-market-share/ . Accessed 11 September 2013.Apple (2013) iTunes. http://www.apple.com/itunes/ . Accessed 11 September 2013.Google (2013) Google play. https://play.google.com/store . Accessed 11 September 2013.Rowinski, D., (2013) The data doesn’t lie: iOS apps are better than android. Readwrite mobile. http://readwrite.com/2013/01/30/the-data-doesnt-lie-ios-apps-are-better-quality-than-android . Accessed 11 September 2013.Rajan, S. P., and Rajamony, S., Viable investigations and real-time recitation of enhanced ECG-based cardiac telemonitoring system for homecare applications: A systematic evaluation. Telemed J E Health 19(4):278–286, 2013.Logan, A. G., Transforming hypertension management using mobile health technology for telemonitoring and self-care support. Can J Cardiol 29(5):579–585, 2013.Tamrat, T., and Kachnowski, S., Special delivery: An analysis of mHealth in maternal and newborn health programs and their outcomes around the world. Matern Child Health J 16(5):1092–1101, 2012.Martínez-Pérez, B., de la Torre-Díez, I., López-Coronado, M., and Herreros-González, J., Mobile Apps in Cardiology: Review. JMIR Mhealth Uhealth 1(2):e15, 2013.de Wit HA, Mestres Gonzalvo C, Hurkens KP, Mulder WJ, Janknegt R, et al., Development of a computer system to support medication reviews in nursing homes. Int J Clin Pharm. 26, 2013.Dahlström, O., Thyberg, I., Hass, U., Skogh, T., and Timpka, T., Designing a decision support system for existing clinical organizational structures: Considerations from a rheumatology clinic. J Med Syst 30(5):325–31, 2006.Lambin P, Roelofs E, Reymen B, Velazquez ER, Buijsen J, et al., ‘Rapid learning health care in oncology’ - An approach towards decision support systems enabling customised radiotherapy’. Radiother Oncol. 27, 2013.Graham, T. A., Bullard, M. J., Kushniruk, A. W., Holroyd, B. R., and Rowe, B. H., Assessing the sensibility of two clinical decision support systems. J Med Syst 32(5):361–8, 2008.Martínez-Pérez, B., de la Torre-Díez, I., and López-Coronado, M., Mobile health applications for the most prevalent conditions by the World Health Organization: Review and analysis. J Med Internet Res 15(6):e120, 2013.Savel, T. G., Lee, B. A., Ledbetter, G., Brown, S., LaValley, D., et al., PTT advisor: A CDC-supported initiative to develop a mobile clinical laboratory decision support application for the iOS platform. Online J Public Health Inform 5(2):215, 2013.Doctor Doctor Inc. (2009) iDoc. iTunes. https://itunes.apple.com/es/app/idoc/id328354734?mt=8 . Accessed 13 September 2013.Hardyman, W., Bullock, A., Brown, A., Carter-Ingram, S., and Stacey, M., Mobile technology supporting trainee doctors’ workplace learning and patient care: An evaluation. BMC Med Educ 13:6, 2013.Lee, N. J., Chen, E. S., Currie, L. M., Donovan, M., Hall, E. K., et al., The effect of a mobile clinical decision support system on the diagnosis of obesity and overweight in acute and primary care encounters. ANS Adv Nurs Sci 32(3):211–21, 2009.Divall, P., Camosso-Stefinovic, J., and Baker, R., The use of personal digital assistants in clinical decision making by health care professionals: A systematic review. Health Informatics J 19(1):16–28, 2013.Chignell, M, and Yesha, Y, Lo, J., New methods for clinical decision support in hospitals. In Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research (CASCON’10). Toronto, ON; Canada, 2010Charani, E., Kyratsis, Y., Lawson, W., Wickens, H., Brannigan, E. T., et al., An analysis of the development and implementation of a smartphone application for the delivery of antimicrobial prescribing policy: Lessons learnt. J Antimicrob Chemother 68(4):960–7, 2013.Klucken, J., Barth, J., Kugler, P., Schlachetzki, J., Henze, T., et al., Unbiased and mobile gait analysis detects motor impairment in Parkinson’s disease. PLoS One 8(2):e56956, 2013.Hervás, R., Fontecha, J., Ausín, D., Castanedo, F., Bravo, J., et al., Mobile monitoring and reasoning methods to prevent cardiovascular diseases. Sensors (Basel) 13(5):6524–41, 2013.Di Noia, T., Ostuni, V. C., Pesce, F., Binetti, G., Naso, N., et al., An end stage kidney disease predictor based on an artificial neural networks ensemble. Expert Syst Appl 40(11):4438–4445, 2013.Velikova, M., van Scheltinga, J. T., Lucas, P. J. F., and Spaanderman, M., Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare. Int J Approx Reason, 2013. doi: 10.1016/j.ijar.2013.03.016 .Medical Data Solutions (2012) Pediatric clinical pathways. Google play. https://play.google.com/store/apps/details?id=com.ipathways . Accessed 17 September 2013.QxMD Medical Software Inc. (2013) Calculate by QxMD. Google play. https://play.google.com/store/apps/details?id=com.qxmd.calculate . Accessed 17 September 2013.Skyscape (2012) ACC pocket guides. Google play. https://play.google.com/store/apps/details?id=com.skyscape.packagefiveepkthreeundata.android.voucher.ui . Accessed 17 September 2013.Skyscape (2013) Skyscape medical resources. Google play. https://play.google.com/store/apps/details?id=com.skyscape.android.ui&hl=en . Accessed 17 September 2013.Pieter Kubben, M.D., (2012) NeuroMind. Google play. https://play.google.com/store/apps/details?id=eu.dign.NeuroMind . Accessed 17 September 2013.Mobile Systems, Inc. (2013) 2013 Medical diagnosis TR. Google play. https://play.google.com/store/apps/details?id=com.mobisystems.msdict.embedded.wireless.mcgrawhill.cmdt2013 . Accessed 17 September 2013.World Health Organization (2013) The global burden of disease: 2004 update. http://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdf . Accessed 18 September 2013.Martínez-Pérez, B., de la Torre-Díez, I., Candelas-Plasencia, S., and López-Coronado, M., Development and evaluation of tools for measuring the Quality of Experience (QoE) in mHealth applications. J Med Syst 37(5):9976, 2013

    Empowering patients in self-management of parkinson's disease through cooperative ICT systems

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    The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies. © 2016, IGI Global. All rights reserved
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