Academic literature on the topic 'Parkinson's Disease Wearable Devices Disease Monitoring mHealth'
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Journal articles on the topic "Parkinson's Disease Wearable Devices Disease Monitoring mHealth"
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Haitham AlAdwani and Zahra ALSiyabi. "A Systematic Review of IoT Integration on Health Monitoring System." International Journal of Engineering and Management Research 13, no. 1 (February 8, 2023): 50–59. http://dx.doi.org/10.31033/ijemr.13.1.6.
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The Internet of Things (IoT) has had a significant impact on many fields, including the healthcare industry. It has, in particular, resulted in the development of devices that can collect and transmit data, allowing for better patient monitoring. IoT has enabled remote patient monitoring and telemedicine, which has significantly improved care. IoT wearable devices can collect and transmit data on patients' blood pressure, heart rates, and blood glucose levels. IoT could also help monitor hand hygiene compliance and track patients' moods and depression. Significantly, monitoring the symptoms of Parkinson's disease patients via IoT aids in disease management. These IoT applications have had significant implications in healthcare. IoT applications reduce healthcare costs while also improving treatment. The diagnosis becomes timely, allowing timely interventions to be implemented. IoT also enables proactive treatment and ensures the effective use and management of drug-related equipment. The main challenges are data security and the high initial implementation cost. In general, implementing IoT has had an impact on care delivery and resulted in better patient outcomes. Keywords— Internet of Things, patient monitoring, heart rates, blood glucose, Parkinson's disease
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Ranjan, Yatharth, Zulqarnain Rashid, Callum Stewart, Pauline Conde, Mark Begale, Denny Verbeeck, Sebastian Boettcher, Richard Dobson, and Amos Folarin. "RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices." JMIR mHealth and uHealth 7, no. 8 (August 1, 2019): e11734. http://dx.doi.org/10.2196/11734.
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Background With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. Objective Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. Methods RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. Results General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. Conclusions RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.
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Ortet, Cláudia, and Liliana Vale Costa. "“Listen to Your Symptoms When the Immune System Is Calling for You”: Monitoring Autoimmune Diseases with the iShU Wearable App." Sensors 22, no. 10 (May 18, 2022): 3834. http://dx.doi.org/10.3390/s22103834.
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The immune system plays a key role in protecting living beings against bacteria, viruses, and fungi, among other pathogens, which may be harmful and represent a threat to our own health. However, for reasons that are not fully understood, in some people this protective mechanism accidentally attacks the organs and tissues, thus causing inflammation and leads to the development of autoimmune diseases. Remote monitoring of human health involves the use of sensor network technology as a means of capturing patient data, and wearable devices, such as smartwatches, have lately been considered good collectors of biofeedback data, owing to their easy connectivity with a mHealth system. Moreover, the use of gamification may encourage the frequent usage of such devices and behavior changes to improve self-care for autoimmune diseases. This study reports on the use of wearable sensors for inflammation surveillance and autoimmune disease management based on a literature search and evaluation of an app prototype with fifteen stakeholders, in which eight participants were diagnosed with autoimmune or inflammatory diseases and four were healthcare professionals. Of these, six were experts in human–computer interaction to assess critical aspects of user experience. The developed prototype allows the monitoring of autoimmune diseases in pre-, during-, and post-inflammatory crises, meeting the personal needs of people with this health condition. The findings suggest that the proposed prototype—iShU—achieves its purpose and the overall experience may serve as a foundation for designing inflammation surveillance and autoimmune disease management monitoring solutions.
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Liu, Yizhi, Zihan Liang, Chengjiang Li, Jiezhou Guo, and Gang Zhao. "An Investigation into the Adoption Behavior of mHealth Users: From the Perspective of the Push-Pull-Mooring Framework." Sustainability 14, no. 21 (November 2, 2022): 14372. http://dx.doi.org/10.3390/su142114372.
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As an important branch of the modern electronic health care services, mobile health applications (mHealth APP) have been widely accepted as a novel health care-providing platform. Based on mobile communications, mHealth is operated on smart terminals such as smart phones, tablet computers, wireless devices or wearable devices, providing multi-channel, multi-terminal and multi-network services. Because mHealth is not restricted by time and space, it serves as a more effective disease management tool for communications between patients and medical workers. In the background of “Internet+”, this study aims to explore the internal adoption behavior of mHealth users to improve the efficiency of medical services, reduce medical costs, and enrich the “Internet + medical health” research. Guided by the push-pull-mooring framework (PPM), this study proposes a conceptual model of mHealth users’ adoption behavior. A specially designed survey was used to collect data on users’ adoption behavior (n = 183). SPSS 25.0 (Guiyang, China) and AMOS 21.0 are used for data analysis. The results show that users’ adoption attitude partially mediates the relationship between the adoption intentions and three key factors (inconvenience, APP attractiveness, and high risk). The adoption intention also partially mediates the relationship between adoption attitude and adoption behavior. Peer influence does not have a direct effect on adoption intention, but it shows a statistically significant indirect effect on adoption intention and adoption behavior through adoption attitude. The negative effect of high switching cost is not significant for both adoption attitude and adoption intention. This study elucidates the internal mechanisms underlying mHealth users’ adoption behavior. The findings can help mHealth providers to arouse more users’ adoption behavior, improve the quality of medical services, and reduce medical costs.
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Kos, Milan, Laurien Buffart, Jan Willem de Groot, Hans Westgeest, Wouter Dercksen, Hanneke W. M. Van Laarhoven, and Martijn G. H. van Oijen. "Comprehensive ambulatory monitoring during immunotherapy in patients with advanced melanoma: A prospective trial (CAMP-IT)." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): TPS1589. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.tps1589.
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TPS1589 Background: The emergence of immune checkpoint inhibitors has improved survival outcomes for patients with advanced melanoma. However, these treatment modalities are also associated with specific immune-related toxicities. These are often reversible after prompt recognition and initiation of appropriate management, but can result in severe morbidity and hamper health-related quality of life (HRQoL) if left undetected. Hence, accurate and regular monitoring of these patients is critical. Recent advances in mHealth technologies and the rapidly expanding armamentarium of wearable devices allow for real-time objective (vital signs and physical activity) data and patient-reported outcome measurement (PROMs) collection and, hence, serve this purpose. We hypothesize that collection of real-time objective data adds to the early detection of disease- and treatment-related adverse events. The primary objective of this study is to determine the feasibility of collecting real-time PROMs, vital signs, and physical activity data in advanced melanoma patients receiving immunotherapy using a comprehensive ambulatory monitoring platform (CAMP) that consists of a smartphone app, activity monitor, digital thermometer, and online dashboard for physicians. Methods: In this prospective multi-center trial, patients (n = 50) with advanced melanoma, scheduled to receive immunotherapy with immune checkpoint inhibitors, and with access to a smartphone are eligible for inclusion. Consenting patients will be asked to wear a FitBit Versa 2.0 during waking hours, collect daily temperature measurements using a Withings Smart Temporal thermometer, and answer weekly toxicity questionnaires (NCI PRO-CTCAE) using the smartphone app for the duration of the study (12 weeks). Primary outcome is feasibility in terms of (i) participation rates, (ii) wear-time, (iii) compliance rates with in-app questionnaires and temperature measurements, and (iv) satisfaction with the platform. Secondary exploratory outcomes include associations between CAMP-derived parameters and clinical outcomes: performance status (PS), HR-QoL scores (EORTC QLQ-C30 questionnaire), unplanned hospitalizations, physician-assessed adverse events, and 1-year survival outcomes. PS and HR-QoL will be rated at baseline, mid-study, and end-of-study. The occurrence of adverse events will be documented up to 12 months from baseline. Survival outcomes will be compared to a propensity score matched group from the Netherlands Cancer Registry. Accrual has started in February 2021. Clinical trial information: NL8827.
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Timotijevic, L., P. Rusconi, C. Hodgkins, B. M. Egan, and A. Banks. "Clinicians’ decision making about Parkinson’s treatment plans using self-report vs digital data." European Journal of Public Health 29, Supplement_4 (November 1, 2019). http://dx.doi.org/10.1093/eurpub/ckz185.279.
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Abstract Background An mHealth-based support systems promise to deliver objective data about the patient’s healthcare status to the clinician in a timely manner but at the same time, risks increasing ‘technical uncertainty”, by increasing the amount of available information, but not necessarily its utility in making medical decisions.The study aim was to investigate clinicians’ decision making about treatment and care plans based on the relative utility of subjective (reported by a person with Parkinson’s Disease, PwP) or objective (digital health) information. Methodology Clinicians completed an online questionnaire with 15 vignettes describing patient cases of PwP where information type (subjective, objective, subjective and objective) and symptoms / signs were manipulated whilst disease stage, duration and patient demographics were kept constant. Dependent variables were the likelihood of changing the care plan and the confidence in the decision. We also recorded the willingness to rely on subjective or objective information. Results Clinicians were equally likely to change the care plan and were equally confident in their decision when receiving information from self-reports or the digital health devices’ outcomes. The likelihood for change increased when both information sources provided consistent information. However, inconsistent information from both sources did lead to more conservative decision making, that is, clinicians were more reluctant to change the care plan. Conclusions Clinicians consider digital health information equally as useful and trustworthy as patients’ self-reports for their care plan decisions. This finding corroborates the potential utility of wearable technology and mobile devices for symptoms’ monitoring. Facilitating clinician’s feedback on situations where subjective and objective sources provide conflicting information is an important consideration for the development and improvement of clinical decision support systems (DSS). Key messages It would be important to identify conditions and mechanisms that could give rise to this conflicting information and the impact that that could have on patient care. Guidelines (e.g., more frequent appointments to monitor the situation) could then be put in place to deal with these situations.
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Choudhari, Ganesh, Prajakta Wagh, Vishnu Choudhari, Ashwini Gawade, Ranjit Gadhave, and Chandrashekhar Bobade. "Wearable Devices and mHealth: The Significant Applications in COVID 19 Pandemic." Journal of Pharmaceutical Research International, March 10, 2022, 9–20. http://dx.doi.org/10.9734/jpri/2022/v34i21b35843.
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Wearable devices (WDs) and mHealth (Mobile Health) give you the healthcare services, overcoming geographical, temporal or even organizational obstacles. Wearable technologies will have non-encroaching and sovereign devices that collect save and examine physiological information that will certainly help to improve patient health. It has been used so far for fitness purposes. But with increasing demand by patients and health care workers, wearable devices have also been developed to monitor patient health-related issues. It collects and analyses data. In some scenarios makes a sensible decision and provide a suitable response to the users. Now a day, mobile applications have also proven effective in the field of medicine with the motto of giving personalized treatment to disease control. Advanced technology in wearable gadgets has become a great aspect of our day-to day life in addition to the health care industry.
A global pandemic that the world is facing in the form of COVID19 has come up with the importance of clinical research and technologies which help to tackle COVID 19 infection worldwide in an exceptional manner. While expected results have been found on effective use of wearable devices and mHealth systems to study the structure of COVID 19 and upcoming infectious ailment. The objective of this review is to test permitting technologies and structures with diverse utility to deal with the COVID 19 disaster. This review acknowledges the researchers of the wearable devices and mHealth systems which proved their significance in the present pandemic. Also, this review explains the exceptional tracking devices, which include heart rate, temperature and oxygen monitoring that, are used to diagnose COVID 19 patients.
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Rodríguez-Martín, Daniel, Joan Cabestany, Carlos Pérez-López, Marti Pie, Joan Calvet, Albert Samà, Chiara Capra, Andreu Català, and Alejandro Rodríguez-Molinero. "A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ONTM." Frontiers in Neurology 13 (June 2, 2022). http://dx.doi.org/10.3389/fneur.2022.912343.
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In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ONTM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ONTM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ONTM are presented.
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Guo, Yao, Jianxin Yang, Yuxuan Liu, Xun Chen, and Guang-Zhong Yang. "Detection and assessment of Parkinson's disease based on gait analysis: A survey." Frontiers in Aging Neuroscience 14 (August 3, 2022). http://dx.doi.org/10.3389/fnagi.2022.916971.
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Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.
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Gil-Martín, Manuel, Cristina Luna-Jiménez, Fernando Fernández-Martínez, and Rubén San-Segundo. "Signal and Visual Approaches for Parkinson's Disease Detection from Spiral Drawings." Proceedings of the Northern Lights Deep Learning Workshop 4 (January 23, 2023). http://dx.doi.org/10.7557/18.6809.
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The development of medical decision-support technologies that provide accurate biomarkers to physicians is an important research area. For example, in the case of Parkinson's Disease (PD), the current supervisions of patients become intrusive, occasional, and subjective. However, new technologies such as wearable devices, signal processing, computer vision, and deep learning could offer a non-intrusive, continuous, and objective solution to help physicians with patient monitoring. The Parkinson's Disease Spiral Drawings public dataset was selected to face PD detection in this work by comparing four representation methods of the X, Y, and the pressure time series: signal, visual, hand-crafted, and fusion. The signal approach uses the Fast Fourier Transform of recording windows and a Convolutional Neural Network for modeling; the visual strategy employs visual transformer features from gray-scale images; the hand-crafted technique utilizes statistics calculated from temporal signals, and the fusion combines the information from the previous approaches. In these procedures, a Random Forest classifier was used for PD detection using the attributes extracted from each type of representation. The best results showed an F1 score of 93.33% and 93.06% at the user level using a signal approach with the three signals for the Static Spiral Task and an image-based proposal with X and Y coordinates for the Dynamic Spiral Task, respectively.
Dissertations / Theses on the topic "Parkinson's Disease Wearable Devices Disease Monitoring mHealth"
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Marcante, Andrea. "PD_manager: an mHealth platform for Parkinson's disease Management." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421871.
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Parkinson’s disease (PD) current clinical management is mostly based on patient’s subjective report about the effects of treatments and on medical examinations that unfortunately represent only a snapshot of a highly fluctuating clinical condition. This traditional approach requires time, it is biased by patient’s judgment and is often not completely reliable, especially in moderate advanced stages. The main purpose of the EU funded project PD_manager (Horizon 2020, Grant Agreement n° 643706) is to build and evaluate an innovative, mHealth, patient-centric system for PD remote monitoring. After a first phase of research and development, a set of wearable devices has been selected and tested on 20 patients. The raw data recorded have been used to feed algorithms necessary to recognize motor symptoms. In parallel, other applications have been developed to test also the main non-motor symptoms. On a second phase, a case- control randomized multicentric study has been designed and performed to assess the acceptability and utility of the PD_manager system at patients’ home, compared to the current gold standard for home monitoring, represented by symptoms diaries. 136 couples of patients and caregivers have been recruited, and at the end of the trial the system was found to be very well tolerated and easy to use, compared to diaries. The developed System is able to recognize motor and non-motor symptoms, helping healthcare professionals in taking decisions on therapeutic strategies. Moreover, PD_manager could represent a useful tool for patient's self-monitoring and self-care promotion.
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"How Does Technology Development Influence the Assessment of Parkinson’s Disease? A Systematic Review." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.53891.
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abstract: Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The pathology for PD is difficult and expensive. Furthermore, it depends on patient diaries and the neurologist’s subjective assessment of clinical scales. Objective, accurate, and continuous patient monitoring have become possible with the advancement in mobile and portable equipment. Consequently, a significant amount of work has been done to explore new cost-effective and subjective assessment methods or PD symptoms. For example, smart technologies, such as wearable sensors and optical motion capturing systems, have been used to analyze the symptoms of a PD patient to assess their disease progression and even to detect signs in their nascent stage for early diagnosis of PD.
This review focuses on the use of modern equipment for PD applications that were developed in the last decade. Four significant fields of research were identified: Assistance diagnosis, Prognosis or Monitoring of Symptoms and their Severity, Predicting Response to Treatment, and Assistance to Therapy or Rehabilitation. This study reviews the papers published between January 2008 and December 2018 in the following four databases: Pubmed Central, Science Direct, IEEE Xplore and MDPI. After removing unrelated articles, ones published in languages other than English, duplicate entries and other articles that did not fulfill the selection criteria, 778 papers were manually investigated and included in this review. A general overview of PD applications, devices used and aspects monitored for PD management is provided in this systematic review.Dissertation/Thesis
Masters Thesis Computer Engineering 2019
Book chapters on the topic "Parkinson's Disease Wearable Devices Disease Monitoring mHealth"
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Rovini, Erika, Dario Esposito, Carlo Maremmani, Paolo Bongioanni, and Filippo Cavallo. "Empowering Patients in Self-Management of Parkinson's Disease Through Cooperative ICT Systems." In Wearable Technologies, 637–63. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5484-4.ch028.
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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.
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Rovini, Erika, Dario Esposito, Carlo Maremmani, Paolo Bongioanni, and Filippo Cavallo. "Empowering Patients in Self-Management of Parkinson's Disease through Cooperative ICT Systems." In Advances in Medical Technologies and Clinical Practice, 251–77. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9530-6.ch010.
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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.
Conference papers on the topic "Parkinson's Disease Wearable Devices Disease Monitoring mHealth"
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Li, Liang, Qian Yu, Baoteng Xu, Qifan Bai, Yunpeng Zhang, Huijun Zhang, Chengjie Mao, Chunfeng Liu, Tianyu Shen, and Shouyan Wang. "Multi-sensor wearable devices for movement monitoring in Parkinson's disease." In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2017. http://dx.doi.org/10.1109/ner.2017.8008294.
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Li, Yan, Qingyuan Bai, Xianjun Yang, Xu Zhou, Yining Sun, and Zhiming Yao. "An abnormal gait monitoring system for patients with Parkinson's disease based on wearable devices." In 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2022. http://dx.doi.org/10.1109/cisp-bmei56279.2022.9980005.
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