Article

The Internet of Things for Basic Nursing Care - A Scoping Review

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Abstract

Background: The novel technology of the Internet of Things (IoT) connects objects to the Internet and its most advanced applications refine obtained data for the user. We propose that Internet of Things technology can be used to promote basic nursing care in the hospital environment by improving the quality of care and patient safety. Objectives: To introduce the concept of Internet of Things to nursing audience by exploring the state of the art of Internet of Things based technology for basic nursing care in the hospital environment. Data sources and review methods: Scoping review methodology following Arksey & O'Malley's stages from one to five were used to explore the extent, range, and nature of current literature. We searched eight databases using predefined search terms. A total of 5030 retrievals were found which were screened for duplications and relevancy to the study topic. 265 papers were chosen for closer screening of the abstracts and 93 for full text evaluation. 62 papers were selected for the review. The constructs of the papers, the Internet of Things based innovations and the themes of basic nursing care in hospital environment were identified. Results: Most of the papers included in the review were peer-reviewed proceedings of technological conferences or articles published in technological journals. The Internet of Things based innovations were presented in methodology papers or tested in case studies and usability assessments. Innovations were identified in several topics in four basic nursing care activities: comprehensive assessment, periodical clinical reassessment, activities of daily living and care management. Conclusions: Internet of Things technology is providing innovations for the use of basic nursing care although the innovations are emerging and still in early stages. Internet of things is yet vaguely adopted in nursing. The possibilities of the Internet of Things are not yet exploited as well as they could. Nursing science might benefit from deeper involvement in engineering research in the area of health.

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... One such method entails the promotion of a culture of safety; another involves improvements to specific aspects of care delivery that staff identify as harmful to patients [23]. Several studies have assessed the recent trend of utilizing IoT in healthcare to improve patient safety [24,25]. Ahmadi et al. (2019) assert that IoT can be used to achieve several goals in hospital management, including preventing infections [1]. ...
... Ahmadi et al. (2019) assert that IoT can be used to achieve several goals in hospital management, including preventing infections [1]. However, despite its various applications in patient safety, IoT still requires more research and experimentation, as most existing research is in the early stage of testing new methodologies [25]. Studies on the practical impacts of IoT on patient safety measures in a hospital setting are sparse. ...
... Thus, this study aims to explore an advanced IoT-based intervention in a hospital setting and empirically demonstrate its impact on patient safety. This research contributes real-life, application-based evidence to validate the claims in the literature that IoT improves patient safety by showing its impact on patient-fall and hygiene-compliance rates [24,25]. We use both pre-post and time-series analyses to demonstrate IoT's impact on patient safety. ...
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Introduction This study evaluates the impact of an Internet of Things (IoT) intervention in a hospital unit and provides empirical evidence on the effects of smart technologies on patient safety (patient falls and hand hygiene compliance rate) and staff experiences. Method We have conducted a post-intervention analysis of hand hygiene (HH) compliance rate, and a pre-and post-intervention interrupted time-series (ITS) analysis of the patient falls rates. Lastly, we investigated staff experiences by conducting semi-structured open-ended interviews based on Roger’s Diffusion of Innovation Theory. Results The results showed that (i) there was no statistically significant change in the mean patient fall rates. ITS analysis revealed non-significant incremental changes in mean patient falls (− 0.14 falls/quarter/1000 patient-days). (ii) HH compliance rates were observed to increase in the first year then decrease in the second year for all staff types and room types. (iii) qualitative interviews with the nurses reported improvement in direct patient care time, and a reduced number of patient falls. Conclusion This study provides empirical evidence of some positive changes in the outcome variables of interest and the interviews with the staff of that unit reported similar results as well. Notably, our observations identified behavioral and environmental issues as being particularly important for ensuring success during an IoT innovation implementation within a hospital setting.
... This includes biological and patient monitoring applications [10]. Mobile technology for vital sign monitoring (blood sugar implants, biosensors) and electronic recording systems will undoubtedly expand the duties of physicians and nurses [3,11,12]. ...
... The Internet of Things (IoT) is intended to aid in monitoring patients, tracking human mobility, and responding to emergencies [12][13][14]. Vital indications will be collected via IoT systems, which will enable interactive patient monitoring in collaboration with healthcare workers. Smart bed systems can be used to monitor hospital occupancy and if patients leave their beds. ...
... Home care will grow in the future. With the expansion of home care services, IoT technology will enable more effective patient follow-up[12,17,18].Digital health has the potential to enable care to be structured around an individual's needs and desires, to improve coordinated care, and to facilitate the sharing of knowledge between patients and health care providers. As a result, the use of wireless devices to provide health care services outside of hospitals is now inevitable. ...
Article
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In the healthcare industry, Internet of Things (IoT) based systems are utilized for phone care delivery, smart healthcare, smart health sensors and wearable technology, preventative systems, and distant monitoring. No studies have assessed nurses’ perspectives of IoT applications in Indonesia. The purpose of this study was to identify health professionals’ perspectives on future health and technology trends, to identify their readiness to adopt new health technologies, and to identify the use of IoT technology in healthcare applications. This study was conducted using a descriptive and cross-sectional approach. Nurses with a Diploma III and at least one year of experience were selected from three different departments (medical surgical, pediatrics, maternity). About 76% of 350 nurses had little knowledge of IoT technology and 95% said they do not keep up to date with IoT publications. About 50% of nurses believed that IoT technology will have a large impact on the health and education sectors. These results indicated that IoT technology and informatics should be included in nursing education and further studies should be conducted to integrate technological trends into healthcare and nursing practices. Keywords: perceptions, nurses, internet of things, healthcare
... These systems are developed to provide proactive healthcare solutions as well as reduce medical costs: e.g., providing efficiency and cost-savings for doctors, nurses, and pharmaceutical companies [1]. Fortunately, rapid advancements in the Internet of Things (IoT)-based systems and wearable devices offer opportunities for the development of health monitoring systems [2]. Such IoT-based healthcare systems can provide comprehensive patient care by leveraging various sensor types, communication units, and computing resources. ...
... corresponding PPG records with the highest precision, recall, or F1-scores (in each row) are presented in bold type.2 The number of signals analyzed for each SNR range are 3580. ...
Article
Full-text available
Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact. Our algorithm is based on convolutional neural networks (CNNs) with dilated convolutions. We train and evaluate the proposed method using a dataset collected via smartwatches under free-living conditions in a home-based health monitoring application. A data generator is also developed to produce noisy PPG data used for model training and evaluation. The method performance is compared against other state-of-the-art methods and is tested with SNRs ranging from 0 to 45 dB. Our method outperforms the existing adaptive threshold, transform-based, and machine learning methods. The proposed method shows overall precision, recall, and F1-score of 82%, 80%, and 81% in all the SNR ranges. In contrast, the best results obtained by the existing methods are 78%, 80%, and 79%. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.
... Hemşirelik bakım hizmetlerinin daha kaliteli ve güvenli sağlanabilmesi için klinik kararların doğru, zamanında ve güncel kli-nik bilgilerle desteklenmesi gerekir. 40 Bakıma muhtaç kişilere yönelik sağlanan bu teknolojik destek, bakımı iyileştiren veya bağımsızlığın sürdürülmesine, iyileştirilmesine veya yeniden kazandırılmasına yardımcı olan sosyal, zihinsel ve fiziksel desteği içerir. 41 Yaşlı bireyin hemşirelik bakımında; ortam destekli yaşam teknolojilerinden yararlanılması, acil yardım sistemleri, yaşamsal belirti izleme veya düşme algılama sistemleri gibi yardımcı teknolojilerin kullanılması, yaşlıların daha uzun süre bağımsız ve aktif kalmalarına yardımcı olmak ve evde bakımını desteklemek için bir çözüm olabilir. ...
... 34 Hemşirenin, yaşlı bakımında yeni teknolojik çözümlerin kullanılmasının faydalarının yanında dikkatle ele alması gereken bazı durumlarda söz konusu olabilir. 40 Yaşlı bireylerin bağımsızlık ve otonomilerini kullanmalarında yardımcı olan yaşam teknolojileri, kaliteli bakım sağlamak için bakım süreci boyunca yaşlıyla ilgili bilgilerin paylaşımına izin vermektedir. Bu durum, yaşlı erişkinler arasında giderek artan ana endişelerden biri hâline gelen mahremiyetin, gizliliğin korunmasında hemşirenin rolünü artırmaktadır. ...
... Even though IoMT and its supporting technologies have been proven to mitigate health problems, such as medical errors, failure, ineffective workflows, and all evident benefits of IoT technologies in the healthcare sector, IoT systems are not fully integrated into healthcare organizations yet [13]. Additionally, IoT developments in the health sector have remained slow in terms of its implementation and adoption in other industries [14]. ...
... The whole issue of IoT technology in healthcare is gaining interest from companies and academics, since it provides a novel method of communicating with healthcare professionals and patients alike. Furthermore, it is a promising instrument to aid the healthcare industry [13]. The purpose of the study is to summarize the literature on factors that might support or prevent health professionals from using IoT technology in their job. ...
... Even though IoMT and its supporting technologies have been proven to mitigate health problems, such as medical errors, failure, ineffective workflows, and all evident benefits of IoT technologies in the healthcare sector, IoT systems are not fully integrated into healthcare organizations yet [13]. Additionally, IoT developments in the health sector have remained slow in terms of its implementation and adoption in other industries [14]. ...
... The whole issue of IoT technology in healthcare is gaining interest from companies and academics, since it provides a novel method of communicating with healthcare professionals and patients alike. Furthermore, it is a promising instrument to aid the healthcare industry [13]. The purpose of the study is to summarize the literature on factors that might support or prevent health professionals from using IoT technology in their job. ...
Article
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In general, the adoption of IoT applications among end users in healthcare is very low. Healthcare professionals present major challenges to the successful implementation of IoT for providing healthcare services. Many studies have offered important insights into IoT adoption in healthcare. Nevertheless, there is still a need to thoroughly review the effective factors of IoT adoption in a systematic manner. The purpose of this study is to accumulate existing knowledge about the factors that influence medical professionals to adopt IoT applications in the healthcare sector. This study reviews, compiles, analyzes, and systematically synthesizes the relevant data. This review employs both automatic and manual search methods to collect relevant studies from 2015 to 2021. A systematic search of the articles was carried out on nine major scientific databases: Google Scholar, Science Direct, Emerald, Wiley, PubMed, Springer, MDPI, IEEE, and Scopus. A total of 22 articles were selected as per the inclusion criteria. The findings show that TAM, TPB, TRA, and UTAUT theories are the most widely used adoption theories in these studies. Furthermore, the main perceived adoption factors of IoT applications in healthcare at the individual level are: social influence, attitude, and personal inattentiveness. The IoT adoption factors at the technology level are perceived usefulness, perceived ease of use, performance expectancy, and effort expectations. In addition, the main factor at the security level is perceived privacy risk. Furthermore, at the health level, the main factors are perceived severity and perceived health risk, respectively. Moreover, financial cost, and facilitating conditions are considered as the main factors at the environmental level. Physicians, patients, and health workers were among the participants who were involved in the included publications. Various types of IoT applications in existing studies are as follows: a wearable device, monitoring devices, rehabilitation devices, telehealth, behavior modification, smart city, and smart home. Most of the studies about IoT adoption were conducted in France and Pakistan in the year 2020. This systematic review identifies the essential factors that enable an understanding of the barriers and possibilities for healthcare providers to implement IoT applications. Finally, the expected influence of COVID-19 on IoT adoption in healthcare was evaluated in this study.
... The concept underlyi this module is to detect the posture change (from lying down to sitting up) of an indiv ual at a high risk of bed falls; this posture change is an indicator of the individual's attem to get out of bed. Accordingly, this module is a response to calls for state-of-the-art I applications for nursing care, as specified in [32]. The proposed BASIC module and application can be implemented in mobile devices for bed fall prevention at home or oth care settings. ...
... The concept underlying this module is to detect the posture change (from lying down to sitting up) of an individual at a high risk of bed falls; this posture change is an indicator of the individual's attempt to get out of bed. Accordingly, this module is a response to calls for state-of-the-art IoT applications for nursing care, as specified in [32]. The proposed BASIC module and its application can be implemented in mobile devices for bed fall prevention at home or other care settings. ...
Article
Full-text available
Accelerometer-based motion sensing has been extensively applied to fall detection. However, such applications can only detect fall accidents; therefore, a system that can prevent fall accidents is desirable. Bed falls account for more than half of patient falls and are preceded by a clear warning indicator: the patient attempting to get out of bed. This study designed and implemented an Internet of Things module, namely, Bluetooth low-energy-enabled Accelerometer-based Sensing In a Chip-packaging (BASIC) module, with a tilt-sensing algorithm based on the patented low-complexity COordinate Rotation DIgital Computer (CORDIC)-based algorithm for tilt angle conversions. It is applied for detecting the postural changes (from lying down to sitting up) and to protect individuals at a high risk of bed falls by prompting caregivers to take preventive actions and assist individuals trying to get up. This module demonstrates how motion and tilt sensing can be applied to bed fall prevention. The module can be further miniaturized or integrated into a wearable device and commercialized in smart health-care applications for bed fall prevention in hospitals and homes.
... The concept of the Internet of Things (IoT) has been gaining momentum since the emergence of wireless technology [1][2][3][4][5][6]. The fundamental premise of IoT is to connect multiple devices through tools like Radio Frequency Identification (RFID), sensors, actuators, smartphones, by which these devices can communicate with each other [7]. ...
... The analysis of the key-route main path enables to reveal more details about the formation of the IoT domain. This study chooses the number of key-routes with a step size 5 (i.e., 5,10,15,20) and eventually selects 20 for the best outcome. The global method is used simultaneously. ...
Article
Full-text available
The Internet of Things (IoT) is a concept that has attracted significant attention since the emergence of wireless technology. The knowledge diffusion of IoT takes place when an individual disseminates his knowledge of IoT to the persons to whom he is directly connected, and knowledge creation arises when the persons receive new knowledge of IoT, which is combined with their existing knowledge. In the current literature, several efforts have been devoted to summarising previous studies on IoT. However, the rapid development of IoT research necessitates examining the knowledge diffusion routes in the IoT domain by applying the main path analysis (MPA). It is crucial to update prior IoT studies and revisit the knowledge evolution and future research directions in this domain. Therefore, this paper adopts the keyword co-occurrence network and MPA to identify the research hotspots and study the historical development of the IoT domain based on 27,425 papers collected from the Web of Science from 1970 to 2020. The results show that IoT research is focused on IoT applications for smart cities, wireless networks, blockchain technology, computing technologies, and AI technologies. The findings from the MPA address the need to explore the knowledge evolution in the IoT domain. They also provide a valuable guide to disseminate the knowledge of IoT among researchers and practitioners, assisting them to understand the history, present and future trends of IoT development and implementation.
... The Internet of Things (IoT) has become a promising solution to improve efficiency or to provide novel solutions in many industrial areas, such as automotive, health-care, home automation, etc [1], [2]. Comprehensive solutions require complex IoT networks of devices capable of interacting with the cloud and other smart objects through the Internet [3]. ...
Preprint
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Complex Internet of Things (IoT) applications such as Healthcare IoT include a variety of compute, data, and communication kernel intensities and have diverse sensitivities of QoS requirements including latency, throughput, availability, accuracy, etc. Ensuring QoS requirements for the applications requires a comprehensive tool to perform efficient full-stack analysis. Per our observation, the literature lacks a simulator capable of supporting a full-stack communication-computation co-simulation of an IoT system. Furthermore, IoT system behavior can dramatically change during run-time due to variation in status and context. Therefore, such a system must be dynamically controlled over time. In this paper, for the first time, we propose a full-stack framework to co-simulate communication and computation aspects of an IoT system in a dynamic scenario. We integrate a Transmission Control Protocol (TCP) latency model with the iFogSim simulator. We conduct a health-care IoT-based case study to evaluate the framework. The framework is open-sourced and available on GitHub in the following repository: https://github.com/HealthSciTech/Dynamic iFogSim.
... It is believed that a lot of innovation in smart healthcare with the proposed software and hardware entities can be used to promote nursing care in health assessment, daily living activities and care management that improve the quality of care and safety at nursing homes. 42 This has been shown by the IoT-based smart devices and systems that interconnect medical resources to provide reliable and effective medical services to elderly people, especially those with chronic illnesses. 43 In addition to enhancing the quality of existing services, smart healthcare could reduce healthcare costs through more efficient use of resources. ...
Article
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Introduction Smart technologies, digital health and eHealth have been shown to enhance institutional elderly care. Because of the rapidly ageing societies, information technologies in geriatric healthcare are urgently needed. A lot of innovation in smart healthcare has occurred in the past decade, and its use in nursing care assessment, daily living activities and service management is yet to be defined. More fundamentally, the concepts, definitions and scopes of a smart nursing home are still vague. Thus, this scoping review aims to examine the extent, range (variety) and nature (characteristics) of evidence on the existing smart concepts and feasible healthcare technologies, types of medical services in nursing home settings and acceptability of a smart nursing home by the elderly people ≥60 years old, their caregivers, nursing home operators and government agencies. Methods and analysis This scoping review will be guided by the smart technology adoption behaviours of elder consumers theoretical model (Elderadopt) by Golant and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews. First, we will conduct an internet search for nursing homes and websites and databases related to the stakeholders to retrieve the definitions, concepts and criteria of a smart nursing home (phase 1). Second, we will conduct an additional systematic electronic database search for published articles on any measures of technological feasibility and integration of medical services in nursing home settings and their acceptability by nursing home residents and caregivers (phase 2). The electronic database search will be carried out from 1999 to 30 September 2020 and limited to works published in English and Chinese languages. For phase 2, the selection of literature is further limited to residents of nursing homes aged ≥60 years old with or without medical needs but are not terminally ill or bed-bound. Qualitative data analysis will follow the Framework Methods and thematic analysis using combined inductive and deductive approaches, conducted by at least two reviewers. Ethics and dissemination This protocol is registered on osf.io (URL: https://osf.io/qtwz2/ ). Ethical approval is not necessary as the scoping review is not a primary study, and the information is collected from selected articles that are publicly available sources. All findings will be disseminated at conferences and published in peer-reviewed journals.
... This method could be employed to imbue undergraduate students with an understanding of specific topics in informatics that may not be possible to introduce into a degree programme. This could add a level of flexibility and be particularly useful where innovative digital tools and applications that are emerging, such as the Internet of Things (Mieronkoski et al., 2017), are taught to ensure nursing students learn about new technological developments. ...
Article
A gap in informatics expertise amongst nursing students, practising staff and faculty has been noted globally, which reduces the potential for nurses to utilise technology to enhance patient care. National nursing education strategies and recommendations from professional associations have identified digital health as an area that needs investment. This case study describes how health informatics is being integrated into a Bachelor of Nursing programme in the United Kingdom. An international collaboration with a US-UK Fulbright Specialist Scholar enabled individual learning units corresponding to key health informatics competencies to be designed and incorporated into a pedagogic framework grounded in the spiral learning approach. This approach is proposed as one way to integrate informatics into nursing education, so students can become competent clinicians that are able to deliver technology enabled care in the health service.
... The Internet of Things (IoT) is a nascent, but rapidly growing paradigm wherein the objects of everyday life are equipped with sensing, processing, storage, communication, and networking capabilities that allow objects to communicate with each other and with users. These objects have become an integral part of the internet [49,50]. In addition, wearable devices (ie, smart wristbands, rings, clothing, etc) form a rapidly emerging new class of IoT technologies named wearable IoT (WIoT) technologies, which have the ability to sense critical physiological, behavioral, and contextual data. ...
Article
Full-text available
Background: Even the same psychological disorders present themselves differently among individuals, underscoring the need for a personalized model approach to the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals experience unique vulnerability to the development of psychopathology given their exposure to repeated stressors and their disruptions in routine, making them a population worthy of investigation. Objective: This prospective study aims to leverage multimodal assessments to examine the feasibility of an individualized approach to understanding contextual factors relevant to changes in daily affect, sleep, physiology, and activities in the service of using event mining to predict changes in mental health. Methods: Recruited participants (expected final N = 20) will be monitored for a period of time (between 3 and 12 months). Participants will download the Personicle application on their smartphone to track activities (e.g., home events, cycling) and be given wearable sensor devices to wear continuously (monitors sleep, physiology, and physical activity). They will be asked to report daily mood and complete weekly open-ended text responses as well as a battery of questionnaires every 3 months. Results: Our study has been approved by the Institutional Review Board and is currently undergoing data collection. Adjustments were made due to the COVID-19 pandemic to enable remote data collection and assess for COVID-19-related stress. Conclusions: This study will help advance the research of individualized approaches to understanding health and well-being through multimodal systems and will demonstrate the benefit of using such approaches to study interrelations between stress, social relationships, technology, and mental health. Clinicaltrial: International registered report: DERR1-10.2196/25775.
... These systems are developed to provide proactive healthcare solutions as well as reduce medical costs: e.g., providing efficiency and cost-savings for doctors, nurses, and pharmaceutical companies [1]. Fortunately, rapid advancement in the Internet of Things (IoT)-based systems and wearable devices offer opportunities for the development of health monitoring systems [2]. Such IoT-based healthcare systems can provide comprehensive patient care by leveraging various sensor types, communication units, and computing resources. ...
Preprint
div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div
... Its high labour intensity, low efficiency, high error rate, and low value embodiment making nurses in a high-tension working state for a long time, resulting in high work pressure and serious job burnout [3]. From the perspective of patients, clinical diagnosis, and treatment, nursing data from collection, analysis, cleaning, and storage to the final service application have obvious periodicity and lag, which cannot provide necessary dynamic data for clinical nursing diagnosis and treatment and affect the accuracy of clinical nursing diagnosis and treatment decision-making and effect [4]. From the perspective of information equipment, restricted by the advanced degree and the relatively fixed mobile range of equipment, the efficiency of medical treatment will be affected to varying degrees in the process of information processing, which will complicate the workflow, and there is a situation that the information process and the medical care process are seemingly inseparable [5]. ...
Article
Full-text available
Internet of things technology began to spread to all industries of our lives; the application of medical internet of things in many hospitals highlighted its advantages and brought a lot of convenience to patients and medical staff. With the continuous progress of China’s medical reform and the continuous improvement of patients’ requirements for medical service quality, this paper discusses the application of medical internet of things in clinical nursing in ward, and the basic information collection, infusion, and mobile nursing were discussed and studied. Through the parallel control study of the laboratory itself, this paper evaluates whether the two different clinical measurement methods of medical internet of things technology and traditional technology are consistent in body temperature, pulse, respiration, and blood oxygen saturation. At the same time, it also deeply studies the value and advantages of internet of things technology in the application of other monitoring indicators in clinical nursing and analyses the problems in its application. The experimental data show that the two measurement methods with different principles can be completely replaced in clinical application, and the time efficiency of the new clinical nursing method under the medical internet of things technology in mapping body temperature, pulse, and respiration has been improved by 76.20% and 72.02%, respectively, surpassing the traditional information technology and realizing the intelligent, automatic, and standardized data acquisition method. It ensures the authenticity of data and the real-time of information flow and meets the needs of resource sharing and medical regional interconnection.
... These systems are developed to provide proactive healthcare solutions as well as reduce medical costs: e.g., providing efficiency and cost-savings for doctors, nurses, and pharmaceutical companies [1]. Fortunately, rapid advancement in the Internet of Things (IoT)-based systems and wearable devices offer opportunities for the development of health monitoring systems [2]. Such IoT-based healthcare systems can provide comprehensive patient care by leveraging various sensor types, communication units, and computing resources. ...
Preprint
div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div
... This study may contribute to a better solution for the rapidly ageing society in China and may be relevant to a broader group of readers interested in this research or industrialised countries that provide nursing homes. It is believed that much innovation of smart technologies can be used and integrated to promote nursing home care within the field of health assessment, activities of daily living and care management that could improve the quality of life and quality of care in nursing homes [103][104][105]. Smart nursing homes will provide the elderly people an alternative solution with independent, safe and comfortable features to home-based care [47,48, [106][107][108][109]. ...
Article
Full-text available
Nursing homes integrated with smart information such as the Internet of Things, cloud computing, artificial intelligence, and digital health could improve not only the quality of care but also benefit the residents and health professionals by providing effective care and efficient medical services. However, a clear concept of a smart nursing home, the expectations and acceptability from the perspectives of the elderly people and their family members are still unclear. In addition, instruments to measure the expectations and acceptability of a smart nursing home are also lacking. The study aims to explore and determine the levels of these expectations, acceptability and the associated sociodemographic factors. This exploratory sequential mixed methods study comprises a qualitative study which will be conducted through a semi-structured interview to explore the expectations and acceptability of a smart nursing home among Chinese elderly people and their family members (Phase I). Next, a questionnaire will be developed and validated based on the results of a qualitative study in Phase I and a preceding scoping review on smart nursing homes by the same authors (Phase II). Lastly, a nationwide survey will be carried out to examine the levels of expectations and acceptability, and the associated sociodemographic factors with the different categories of expectations and acceptability (Phase III). With a better understanding of the Chinese elderly people’s expectations and acceptability of smart technologies in nursing homes, a feasible smart nursing home model that incorporates appropriate technologies, integrates needed medical services and business concepts could be formulated and tested as a solution for the rapidly ageing societies in many developed and developing countries.
... The increasing availability of wearables, interconnected devices capable of acquiring high-quality biosignals, opens important opportunities for advanced machine learningenabled health monitoring and intervention applications [3], [4]. Recent literature [5] demonstrates that it is indeed possible to objectively detect stress by analyzing biological signals. ...
Preprint
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Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies. Although several studies have evaluated stress levels in controlled settings, objective stress assessment in everyday settings is still largely under-explored due to challenges arising from confounding contextual factors and limited adherence for self-reports. In this paper, we explore the objective prediction of stress levels in everyday settings based on heart rate (HR) and heart rate variability (HRV) captured via low-cost and easy-to-wear photoplethysmography (PPG) sensors that are widely available on newer smart wearable devices. We present a layered system architecture for personalized stress monitoring that supports a tunable collection of data samples for labeling, and present a method for selecting informative samples from the stream of real-time data for labeling. We captured the stress levels of fourteen volunteers through self-reported questionnaires over periods of between 1-3 months, and explored binary stress detection based on HR and HRV using Machine Learning Methods. We observe promising preliminary results given that the dataset is collected in the challenging environments of everyday settings. The binary stress detector is fairly accurate and can detect stressful vs non-stressful samples with a macro-F1 score of up to \%76. Our study lays the groundwork for more sophisticated labeling strategies that generate context-aware, personalized models that will empower health professionals to provide personalized interventions.
... IoT has also enabled the basic nursing capable of intelligent decision-making. In hospitals patient's vitals can be monitored in real-time and this does not require an all-time manned attendance by the nurses [16,17]. decision-making approaches, which subsequently minimizes mistakes [18]. ...
Chapter
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The healthcare sector has been stretched to its maximum capacity with the outbreak of coronavirus. With a worldwide impact it has highlighted the maximum capabilities and limitations in the existing healthcare facilities worldwide. Such an unprecedented load of healthcare requirements can be supported with Internet of Things (IoT) and cloud services. The limited number of healthcare staff and limited resources urge the use of emerging technologies to support healthcare provision. In such circumstance IoT and cloud computing offer sufficient potential that can be used for monitoring, diagnosis, support, and intelligent decisioning. A smart environment can be created with the collected data from patients, medical equipment, hospitals, ambulances, recovery centers, old-age houses, and nursing homes. The use of cloud services integrated with IoT allows the collection, analysis, and provisioning of support and solution at a rapid rate. This facilitates remote patient-monitoring and safeguards healthcare providers from coming in direct contact with the patient and highly infectious and contagious work environment. It also brings in great potentials for smart-health solutions with enhanced patient observation, monitoring, support, and prediction of vital needs essential in emergency situations.
... These systems are developed to provide proactive healthcare solutions as well as reduce medical costs: e.g., providing efficiency and cost-savings for doctors, nurses, and pharmaceutical companies [1]. Fortunately, rapid advancement in the Internet of Things (IoT)-based systems and wearable devices offer opportunities for the development 5 of health monitoring systems [2]. Such IoT-based healthcare systems can provide comprehensive patient care by leveraging various sensor types, communication units, and computing resources. ...
Preprint
div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div
... Advancements in consumer wearable technology provide opportunities to extend sleep monitoring to mid-or long-term home-based health care applications using low-power, miniaturized, and fashionable wearables [15][16][17]. Wearable electronics and the Internet of Things-based systems are growing dramatically and are expected to revolutionize health care delivery and outcomes [18,19]. In particular, smart rings will most likely become popular in sleep studies. ...
Article
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Background Assessment of sleep quality is essential to address poor sleep quality and understand changes. Owing to the advances in the Internet of Things and wearable technologies, sleep monitoring under free-living conditions has become feasible and practicable. Smart rings and smartwatches can be employed to perform mid- or long-term home-based sleep monitoring. However, the validity of such wearables should be investigated in terms of sleep parameters. Sleep validation studies are mostly limited to short-term laboratory tests; there is a need for a study to assess the sleep attributes of wearables in everyday settings, where users engage in their daily routines. Objective This study aims to evaluate the sleep parameters of the Oura ring along with the Samsung Gear Sport watch in comparison with a medically approved actigraphy device in a midterm everyday setting, where users engage in their daily routines. Methods We conducted home-based sleep monitoring in which the sleep parameters of 45 healthy individuals (23 women and 22 men) were tracked for 7 days. Total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) of the ring and watch were assessed using paired t tests, Bland-Altman plots, and Pearson correlation. The parameters were also investigated considering the gender of the participants as a dependent variable. Results We found significant correlations between the ring’s and actigraphy’s TST (r=0.86; P<.001), WASO (r=0.41; P<.001), and SE (r=0.47; P<.001). Comparing the watch with actigraphy showed a significant correlation in TST (r=0.59; P<.001). The mean differences in TST, WASO, and SE of the ring and actigraphy were within satisfactory ranges, although there were significant differences between the parameters (P<.001); TST and SE mean differences were also within satisfactory ranges for the watch, and the WASO was slightly higher than the range (31.27, SD 35.15). However, the mean differences of the parameters between the watch and actigraphy were considerably higher than those of the ring. The watch also showed a significant difference in TST (P<.001) between female and male groups. Conclusions In a sample population of healthy adults, the sleep parameters of both the Oura ring and Samsung watch have acceptable mean differences and indicate significant correlations with actigraphy, but the ring outperforms the watch in terms of the nonstaging sleep parameters.
... Around 2016, IoT cases in education and practice in the field of nursing information began to be reported [2]. In 2017, a review by Mieronkoski et al. [3] identified four types of nursing IoT: comprehensive assessment, periodic clinical reassessment, activities of daily living, and care management. Papers on the evaluation of nursing information systems using IoT began to emerge in 2018 [4], and a survey in 2019 by a nursing student reported positive intentions to implement nursing practices based on IoT [5]. ...
Chapter
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The purpose of this study was to investigate the perceptions of nursing managers about adopting nursing practices based on the Internet of Things and to examine related ethical issues. Questionnaires were sent to 538 nursing managers in Japan, with 131 responses. Of these, 87% and 33% agreed that a system using radio frequency identifiers would be useful for locating patients and nurses, respectively, 58%–81% recognized the value for patient safety of various camera systems for nursing observation, such as cameras linked to biometric alarms, 73% agreed the usefulness of automatically prioritizing alarms, but only around 39% were in favor of using facial recognition to help nursing observation. Many nursing managers expressed concerns about privacy. Data storage for at least 6 months was supported by 53% for location data and 41% for ceiling camera videos. Thus, nursing practice based on the Internet of Things is widely accepted in Japan.
... The main health IoT application were related to real-time monitoring of patient data using mobile and wireless sensors to track vital signs [62 [63] [64], elderly related risks [65] [66] or for outpatient prenatal management of pregnant women [67]. Basic nursing care can be optimized by using IoT [68], as well as monitoring patients during exercise [69], during rehabilitation procedures [70], tracking their location [71] and monitoring patients to prevent sleep apnea [72]. With real time data monitoring, studies addressed the application of IoT identify cancer diagnosis [73] [74], interpretation of imaging tests [75] and detection of stroke detection [76]. ...
Conference Paper
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Healthcare sector makes use of technological advances to develop value to its processes and offers excellence, safety and accessibility services to patients, workers, healthcare operations and quality management. Thus, Industry 4.0 enabling technologies provide benefits in the management of services and healthcare. This study aims to identify researches that show possible applications of Enabling Technologies of Industry 4.0 in the healthcare area. This research work carried out a bibliographic review of articles that investigated applications of Industry 4.0 enabling technologies of in the field of Health. The selected studies deal primarily with the application of Industry 4.0 Enabling Technologies for activities related to data management, analysis, treatment and sharing healthcare information. The contribution of this study focuses on providing information on the advances of Industry 4.0 and its technologies enabled in the healthcare sector, allowing the prospecting of new opportunities for action and applications of technologies that already exist in the healthcare context.
... To date, IoT application studies for health care use include IoT devices for tracking human activities in primary health care centers [12], for medication compliance among older outpatients [13,14], for intensive health guidance among outpatients with diabetes mellitus [15], and for home-based health care [16]. Although research activities on the use of IoT devices to support long-term care for older adults exist, there is limited evidence on the effectiveness of these interventions among formal caregivers in nursing homes [17][18][19]. In this study, we investigate whether sleep state sensors for supporting long-term care can reduce the mental burden of formal caregivers in a nursing home. ...
Article
Background: Increasing need for nursing care has led to the increased burden on formal caregivers, with those in nursing homes having to deal with exhausting labor. Although research activities on the use of internet of things devices to support nursing care for older adults exist, there is limited evidence on the effectiveness of these interventions among formal caregivers in nursing homes. Objective: This study aims to investigate whether mat-type sleep state sensors for supporting nursing care can reduce the mental burden of formal caregivers in a nursing home. Methods: This was a quasi-experimental study at a nursing home in Tokyo, Japan. The study participants were formal caregivers who cared for residents in private rooms on the fourth and fifth floors of the nursing home. In the intervention group, formal caregivers took care of residents who used sleep state sensors on the fourth floor of the nursing home. The sleep state sensors were mat types and designed to detect body motion such as the frequency of toss and turning and to measure heartbeat and respiration. One sensor was placed on a bed in a private room. When body motion is detected, the information is instantly displayed on a monitor at a staff station. In addition, the mental condition of the formal caregivers was measured using a validated self-reported outcome measure-the Profile of Mood States (POMS), Short-Form, 2nd edition. Formal caregivers in both groups received the POMS at baseline, midpoint (week 4), and endpoint (week 8) to identify changes in these domains. The primary outcome was the difference in total mood disturbance (TMD) of the POMS at baseline and week 8. Results: Of the 22 eligible formal caregivers, 12 (intervention group) utilized sleep state sensors for 8 weeks. The remaining 10 formal caregivers (control group) provided nursing care as usual. As for the primary outcome of the difference between TMD at baseline and week 8, TMD in the intervention group improved by -3.67 versus 4.70 in the control group, resulting in a mean difference of -8.37 (95% CI -32.02 to 15.29; P=.48) in favor of the intervention. Conclusions: The present 8-week study showed that sleep state sensing for elderly residents might not be associated with reduced mental burdens on formal caregivers in nursing homes.
... e NRS 2002 evaluation results in this study showed that the proportion of patients in the H2H group who were malnourished or at risk of nutrition improved significantly after nursing. Another research suggests that the Internet can better realize effective interaction and resource sharing between nursing staff and patients, give patients individualized guidance, correct patients' unhealthy conditions timely and effectively, improve patients' physical and psychological conditions, and enhance patients' cure confidence [27,28]. In this study, the SF-36 short form was adopted to evaluate, and it was found that the scores of mental function, physiological function, social relationship, and treatment status of patients in the H2H group were better than those of the control group after 6 months of nursing, so the patients in H2H group were more satisfied with the nursing. ...
Article
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The objective of this study was to evaluate the application value of “Internet + hospital-to-home (H2H)” nutritional care model using the improved wavelet transform algorithm based on computed tomography (CT) images in the nutritional care management of chronic kidney disease (CKD) stages 3-5. A total of 120 patients with CKD were the research objects and they were randomly divided into two groups. The normal nutritional nursing model was used for nursing of patients in the control group, and the “Internet + H2H″ model was used for the observation group (H2H group), with 60 cases in each group. The nursing effect was evaluated using 320-slice volume CT low-dose perfusion imaging images, anthropometry, laboratory biochemical tests, and other survey scores. The results showed that compared with the mean filter denoising (MFD) algorithm and the orthogonal wavelet denoising (OWD) algorithm, the mean square error (MSE) and signal noise ratio (SNR) values of the IWT algorithm were better (40.0781 vs 45.2891, 59.2123)/(20.0122 vs 18.2311, 15.7812) (P
... As a scoping review related to IoT in nursing education, various review studies were conducted. These studies included reviews on IoT for basic nursing care focusing on hospital environments 14 and in the medical field, mainly prototyping IoT technology and home care for patients. 15 In addition, a literature review on the benefits and challenges of IoT used in the educational field until 2017 8 and a systematic literature review of how IoT was implemented in academic institutions until 2017 16 were carried out. ...
Article
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Introduction Future nursing education needs to build a cutting-edge technology-based educational environment to provide a variety of consumer-oriented education. Thus, the sharing of information in nursing education needs to be considered, especially given the advancement of internet of things (IoT) technology. Before developing a horizontal platform, understanding previously developed IoT platforms is necessary to establish services and devices compatible with each other in different service areas. This scoping review aims to explore the technology used in the IoT platform for the education of nursing students in the undergraduate nursing curriculum. Methods and analysis A preliminary search was completed to find initial search terms, on which a full-search strategy was developed. Search results yielded from PubMed (NCBI) were screened to ensure articles were peer-reviewed, published in English from January 1999 to August 2021, and relevant to developing, applying and evaluating IoT platforms at educational institutions for students in undergraduate nursing programmes. A full-text review of relevant articles will be conducted, and data will be extracted using the developed extraction tool. The extracted qualitative data will be analysed using a modified grounded theory approach, informing a working definition of the IoT platform and related terms. Ethics and dissemination The study was exempted from ethical review by the Institutional Review Board of Nambu University, South Korea. Study results will be disseminated through peer-reviewed journals.
... In [5] NEWS was evaluated against a range of outcomes that are of major importance to patients and staff. It demonstrates a good ability to discriminate patients at risk of the combined outcome of cardiac arrest, unanticipated ICU admission or death within 24 h, which provides ample opportunity for an appropriate clinical intervention to change patient outcome [6]. ...
... With regards to specialized medical care, IoT technology has been used to cater to the need of cardiovascular [87] , cardiopulmonary [87] and ophthalmology [321] . With regards to different categories of populations, IoT has been used to help to monitor indicators related to women's health [322] , including pregnancy [323] , soldiers at the country borders [154] , nursing care at the hospitals [324] , the elderly population in the long-term-care homes [325] , persons with neurological conditions at the rehabilitation center [316] , and also for persons with respiratory complaints including asthma [163] . ...
Thesis
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Background: Despite the presence of robust global public health surveillance mechanisms, the COVID-19 pandemic devastated the world and exposed the weakness of the public healthcare systems. Public health surveillance has improved in recent years as technology evolved to enable the mining of diverse data sources, for example, electronic medical records, social media, to monitor diseases and risk factors. However, the current state of the public health surveillance system depends on traditional (e.g., Canadian Community Health Survey (CCHS), Canadian Health Measures Survey (CHMS)) and modern data sources (e.g., Health insurance registry, Physician billing claims database). While improvement was observed over the past few years, there is still a room for improving the current systems with NextGen data sources (e.g., social media data, Internet of Things data), improved analytical mechanism, reporting, and dissemination of the results to drive improved policymaking at the national and provincial level. With that context, data generated from modern technologies like the Internet of Things (IoT) have demonstrated the potential to bridge the gap and be relevant for public health surveillance. This study explores IoT technologies as potential data sources for public health surveillance and assesses their feasibility with a proof of concept. The objectives of this thesis are to use data from IoT technologies, in this case, a smart thermostat with remote sensors that collect real-time data without additional burden on the users, to measure some of the critical population-level health indicators for Canada and its provinces. Methods: This exploratory research thesis utilizes an innovative data source (ecobee) and cloud-based analytical infrastructure (Microsoft Azure). The research started with a pilot study to assess the feasibility and validity of ecobee smart thermostat-generated movement sensor data to calculate population-level indicators for physical activity, sedentary behaviour, and sleep parameters for Canada. In the pilot study, eight participants gathered step counts using a commercially available Fitbit wearable as well as sensor activation data from ecobee smart thermostats. In the second part of the study, a perspective article analyzes the feasibility and utility of IoT data for public health surveillance. In the third part of this study, data from ecobee smart thermostats from the “Donate your Data” program was used to compare the behavioural changes during the COVID-19 pandemic in four provinces of Canada. In the fourth part of the study, data from the “Donate your Data” program was used in conjunction with Google residential mobility data to assess the impact of the work-from-home policy on micro and macro mobility across four provinces of Canada. The study's final part discusses how IoT data can be utilized to improve policy-level decisions and their impact on daily living, with a focus on situations similar to the COVID-19 pandemic. Results: The Spearman correlation coefficient of the step counts from Fitbit and the number of sensors activated was 0.8 (range 0.78-0.90; n=3292) with statistically significant at P < .001 level. The pilot study shows that ecobee sensors data have the potential to generate the population-level health indicators. The indicators generated from IoT data for Canada, Physical Activity, Sleep, and Sedentary Behaviours (PASS) were consistent with values from the PASS indicators developed by the Public Health Agency of Canada. Following the pilot study, the perspective paper analyzed the possible use of the IoT data from nine critical dimensions: simplicity, flexibility, data quality, acceptability, sensitivity, positive predictive value, representativeness, timeliness, and stability. This paper also described the potential advantages, disadvantages and use cases of IoT data for individual and population-level health indicators. The results from the pilot study and the viewpoint paper show that IoT can become a future data source to complement traditional public health surveillance systems. The third part of the study shows a significant change in behaviour in Canada after the COVID-19 pandemic and work-from-home, stay at home and other policy changes. The sleep habits (average bedtime, wake-up time, sleep duration), average in-house and out-of-the-house duration has been calculated for the four major provinces of Canada (Ontario, Quebec, Alberta, and British Columbia). Compared to pre-pandemic time, the average sleep duration and time spent inside the house has been increased significantly whereas bedtime, and wake-up-time got delayed, and average time spent out-of-the-house decreased significantly during COVID-19 pandemic. The result of the fourth study shows that the in-house mobility (micro-mobility) has been increased after the pandemic related policy changes (e.g., stay-at-home orders, work-from-home policy, emergency declaration). The results were consistent with findings from the Google residential mobility data published by Google. The Pearson correlation coefficient between these datasets was 0.7 (range 0.68-0.8) with statistically significant at P <.001 level. The time-series data analysis for ecobee and google residential mobility data highlights the substantial similarities. The potential strength of IoT data has been demonstrated in the chapter in terms of anomaly detection. Discussion and Conclusion: This research's findings demonstrate that IoT data, in this case, smart thermostats with remote motion sensors, is a viable option to measure population-level health indicators. The impact of the population-level behavioural changes due to the COVID-19 pandemic might be sustained even after policy relaxation and significantly affects physical and mental health. These innovative datasets can strengthen the existing public health surveillance mechanism by providing timely and diverse data to public health officials. These additional data sources can offer surveillance systems with near-real-time health indicators and potentially measure the short- and long-term impact of policy changes.
... The Internet of Things (IoT) has become a promising solution to improve efficiency or to provide novel solutions in many industrial areas, such as automotive, health-care, home automation, etc [1], [2]. Comprehensive solutions require complex IoT networks of devices capable of interacting with the cloud and other smart objects through the Internet [3]. ...
... Internet of Things (IoT) technology is a growing innovation in everyday life [1], but in nursing healthcare, it is still in an emerging and early developmental phase [2]. Overall, the development of information and communication technologies (ICT) in nursing science-and in particular in telenursing-is an urgent need, the prominent reasons for which include the shortage of nursing staff, which is expected to be half (13 million) of the current estimated global workforce within a decade [3]. ...
Article
Full-text available
Background: Existing surveys on telenursing refer to specific areas of nursing after the implementation of a programme, but telenursing in general has not been fully evaluated from a prospective approach. Aim: Design and statistical validation of a telenursing questionnaire. Methods: A new questionnaire was designed with 18 paired (to avoid leading) questions (Likert-5) plus three dichotomous questions (randomly ordered, inspired by existing validated tests) to analyse the dimensions of: acceptance, usefulness and appropriateness of telenursing from the nursing point of view (7 min test). The questionnaire was validated by classical tests and item response tests (Rasch) using six computer-generated databases with different response profiles (tendency to be positioned against, neutral and positioned in favour) with two degrees of agreement between each pair of responses for each option. Results: Classical testing: Cronbach’s alphas (from 0.8 to 0.95), Kaiser–Meyer–Olkin (KMO) (0.93 to 0.95) and a significant p < 0.0001 for Bartlett’s test of sphericity were obtained. Rasch analysis: Reliability coefficients (0.94). Warm’s mean weighted likelihood estimates (0.94). Extreme infit-t and outfit-t values (+1.61 to −1.98). Conclusions: Both the classical test and the Rasch approaches confirm the usefulness of the new test for assessing nurses’ positioning in relation to telenursing.
... It demonstrates a good ability to discriminate patients at risk of the combined outcome of cardiac arrest, unanticipated ICU admission or death within 24h, which provides ample opportunity for an appropriate clinical intervention to change patient outcome. [6] NEWS2 could be made safer for patients with hypercapnic respiratory failure by having two scoring systems for (saturation pulse oxygen) SpO2: ...
Chapter
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With the advent of the Internet of Things (IoT), various interconnected objects can be used to improve the collection and the process of vital signs with partially or fully automatized methods in smart hospital environment. The vital signs data are used to evaluate patient health status using heuristic approaches, such as the early warning scoring (EWS) approach. Several applications have been proposed based on the early warning scores approach to improve the recognition of patients at risk of deterioration. However, there is a lack of efficient tools that enable a personalized monitoring depending on the patient situations. This paper explores the publish-subscribe pattern to provide a self-adaptative early warning score system in smart hospital context. We propose an adaptative configuration of the vital sings monitoring process depending on the patient health status variation and the medical staff decisions.
... The work of [6], presents a review on fog computing within healthcare, exploring different applications through use cases presented in the literature. Another relevant survey is the article of [13], introducing the concept of the Internet of Things to the medical audience by exploring the state of the art of IoT based technology for primary healthcare in the hospital environment. All of these articles explore the fog computing within the health field of study, but they do not propose a taxonomy for the area. ...
Article
Currently, technology greatly benefits the area of healthcare. Modern computers can quickly process a large volume of patient health records. Due to recent advances in the area of Internet of Things and healthcare, patient data can be dispersed in multiple locations. As a result, scientists have been proposing solutions based on Cloud Computing to manage healthcare data. However, suchs solutions present challenges regarding access latency, context-awareness, and large volumes of data. There is an increased probability of processing and transmission errors are more likely to occur as health data sets become larger and more complex. In this context, Fog Computing presents itself as an alternative to reduce health data management complexity, consequently increasing its reliability. To that end, it is important to comprehend the associated challenges before defining a Fog Computing-based architecture to manage healthcare data. This article presents a systematic literature review of fog computing being applied to healthcare area. We propose a taxonomy to explore the open issues and most important challenges on these fields of study. We selected 1070 scientific articles published in the last 10 years, filtering the 44 most significant works for an in-depth analysis. We found that there is several challenges to be addressed such as interoperability, privacy, security, data processing, management of resources and Big Data issues. Also, our contribution include developing a taxonomy for the Fog Computing and healthcare fields of study and finding out challenges and open questions of this area.
... The cloud layer can be obtained either via Internet connected remote servers or by local servers connected to local hospital information system (HIS) to provide more services such as data analytics and security [16]. ...
Article
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Abstract Changes in vital signs are an important indicator of physiological decline and provide opportunities for early recognition and intervention. The collected vital signs data can be evaluated using several approaches such as the Early warning score (EWS) approach to predict the risk level of patients. By exploring the Internet of things (IoT), vital signs monitoring solutions are automated based on various medical devices and sensors. However, there is a lack of efficient tools that enable an adaptative monitoring depending on the patient situations. This article explores the IoT technologies to provide an EWS system in smart hospital situation. The proposed solution presents an adaptative configuration of the vital signs monitoring process depending on the patient’s health status variation and the medical staff decisions. Also, an intelligent notification mechanism that reduces the delay of the medical staff intervention in the case of risk detection is proposed.
Article
Healthcare is an area that is supposed to have significant impacts on the blockchain (BC). Nevertheless, researchers and experts in health informatics are researching and developments in the field. Work is very new in the field but is progressively increasing. It can be referred as uprising in the digital world because of its effective performance concerning security, efficacy, and productivity of different frameworks. It operates as a distributed database, exchanged within a decentralized computer network. It stores data of the transactions performed using crypto-currency and information is managed using various computers linked to a peer-to-peer network. Besides, no trusted third parties are required in a distributed e-Health environment and support document removal. Health care is one of the most important aspects that require efficacious technologies to stay up to date and analyze the population's health status so that medical data can be maintained. Thus, in this paper, a broad review is presented that delineates the usage of conventional healthcare systems and also the use of technology to make it e-healthcare. Further, the role of the blockchain is presented in this paper. Although, a plethora of studies have been proposed that included various methodologies of blockchain in the healthcare industry, yet these studies are not enough to portray the effectiveness of these proposed approaches. In the review, different applications of blockchain that are being utilized at the present time are demonstrated along with the benefits and limitations of blockchain. It is observed that there is a scope of research in this field to know to enhance the utility of blockchain in healthcare. Moreover, an ontology-based Adverse Drug event system can be taken into consideration to use it as a BC application.
Chapter
The demographic changes in the society have led to an urge for new sustainable ways to provide high-quality care for a large group of people with complex health needs. In the Internet of Thing (IoT) technology, the “things”—devices, sensors, and actuators connected to the Internet—collect health-related data from multiple sources with wearable and environmental sensors. Through the wireless connection to the cloud and the automatization of data processing, the system can provide real-time, continuous remote health monitoring. The smart health services are aimed to provide safe and personalized care regardless of the healthcare environment. The potentials of IoT include early detection of on-going and prospective diseases as well as tracking healthy lifestyle habits.
Chapter
After going through a busy and a strenuous day of life, all a person need is a night of good sleep. One needs nothing more but a comfy bed to rest and embrace into. When one has a night of sound sleep, the day starts fresh and blooming. One’s mind finds itself at peace even in chaos. One thing that would help is a good bed. Currently in the market we only find quality cotton cots or made of some material that is suitable to sleep on according to our preference. Now in this smart world, what if that bed becomes smart? The aim of this paper is to manifest an idea of a smart bed companion that not only gives us good sleep but analyses sleep patterns and also controls many other applications and devices accordingly, also its unique factor (novel) to necessitate a person to wake-up. So this smart bed doesn’t make us lazy but helps us to roll out on time.
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Accurate and early diagnosis of COVID-19 can reduce the mortality rate caused by the disease across the globe. Computer-aided diagnosis (CAD) helps radiologists efficiently extract and diagnose the abnormal portions. The healthcare market is currently experiencing rapid development owing to the Internet of Things (IoT). This paper proposes a framework that integrates machine learning and intelligence-based e-Health service systems that can be used as an application of the Internet of Medical Things (IoMT) for the early diagnosis of COVID-19 disease. This framework consists of a classification approach for diagnosing the abnormalities in lung CT images using a whale optimisation algorithm (WOA) optimised wavelet neural network (WNN). WOA optimises the input features, initial weights, hidden nodes, momentum constant, and learning parameters of a WNN in the proposed system. The proposed approach extracts the Laws 16 Texture Energy Measures (LTEM) from the preprocessed CT lung images and classifies the abnormal regions with the help of a WNN classifier. The proposed framework is evaluated using a publicly available COVID-19 dataset that contains both theCOVID-19 and non-COVID-19 cases. The result shows that theproposed approach has a sensitivity of 82%, a specificity of 73.3%, and an accuracy of 84.8%.
Article
Real-time remote health monitoring is dramatically growing, revolutionizing healthcare delivery and outcome in everyday settings. Such remote services enable monitoring individuals anywhere and anytime, allowing diseases early detection and prevention. Photoplethysmography (PPG) is a non-invasive and convenient technique that enables tracking vital signs such as heart rate, heart rate variability, respiration rate, and blood oxygen saturation. PPG is broadly used in various clinical and commercial wearable devices, as it is easy-to-implement and low-cost. However, the technique is highly susceptible to motion artifacts and environmental noises, which distort the collected signals. Therefore, the signal quality needs to be investigated, and unreliable signals should be discarded. In the literature, rule-based and machine learning-based PPG quality assessment methods have been investigated in several studies. However, the rule-based methods are mostly inaccurate in remote health monitoring, where users engage in different physical activities. The supervised machine learning-based methods –including deep learning–are also infeasible for real-time monitoring applications since they are slow and are dependent on a massive pool of annotated data to train the model. In this paper, we introduce a PPG quality assessment method, enabled by an elliptical envelope, which requires low computational resources. The method clusters the PPG signals into two groups as “reliable” and “unreliable.” We also investigate various features extracted from the PPG signals. Five features with the highest scoring values are selected to be fed to the elliptical envelope model. Moreover, we assess the performance of the proposed method in terms of accuracy and execution time, using data collected in free-living conditions via an Internet-of-Things-based health monitoring system enabled by smart wristbands. The method is evaluated in comparison to a state-of-the-art PPG quality assessment method. We also provide the model implemented in Python for the community to be used in their solutions.
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RESUMO Objetivo: Discutir sobre o Cuidado Clínico e o Cuidado Clínico de Enfermagem, neste percurso, propondo uma definição para esses conceitos. Métodos: Trata-se de um estudo teórico, de caráter reflexivo, ancorado na concepção de cuidado e de clínica de autores oriundos de diferentes áreas do conhecimento (Filosofia, Sociologia, Medicina e Enfermagem), com vistas a permitir a construção de um arcabouço teórico capaz de fundamentar as discussões e definições propostas. Resultados: Cuidado Clínico e Cuidado Clínico de Enfermagem têm sido expressões cada vez mais utilizadas por profissionais e pesquisadores. No entanto, estes conceitos ainda se apresentam pouco delimitados. Hegemonicamente, a clínica tem sido representada pelas atividades de investigação e terapêutica das repercussões da doença no corpo, este marcado por sinais e sintomas. Porém, nessa acepção, com foco na doença, a clínica pouco contribui para a resolução dos problemas na saúde. Conclusão: Propõe-se que o Cuidado Clínico represente um novo campo conceitual, com estabelecimento de novas relações entre os sujeitos, que se detenha aos sinais e sintomas, mas também às repercussões psicológicas e sociais do adoecimento, considerando centralmente a pessoa e o seu contexto de vida, e não a doença. ABSTRACT Objective: To discuss about Clinical Care and Clinical Nursing Care, in this path, proposing a definition for these concepts. Methods: It is a theoretical study, reflective, anchored in the conception of care and clinic by several authors from different areas of knowledge (Philosophy, Sociology, Medicine and Nursing), with a view to allowing the construction of a theoretical basis capable of supporting the proposed discussions and definitions. Results: Clinical Care and Clinical Nursing Care have been increasingly used by professionals and researchers. However, these concepts are still poorly defined. Hegemonically, the clinic has been represented by the research and therapeutic activities of the repercussions of the disease on the body, marked by signs and symptoms. However, in this sense, focusing on the disease, the clinic contributes little to the resolution of health problems. Conclusion: It is proposed that the Clinical Care represents a new conceptual field, with the establishment of new relationships between the subjects, which focuses on the signs and symptoms, but also on the psychological and social repercussions of the disease, considering the person, to the detriment of the patient. disease, and its context of life. RESUMEN Objetivo: Discutir la Atención clínica y la Atención de enfermería clínica, a lo largo de este camino, proponiendo una definición para estos conceptos. Métodos: Estudio teórico, reflexivo, anclado en la concepción del cuidado y la clínica por varios autores de diferentes áreas de conocimiento (Filosofía, Sociología, Medicina y Enfermería), con miras a permitir la construcción de una base teórica capaz de sustentar la discusiones y definiciones propuestas. Resultados: La atención clínica y la atención de enfermería clínica han sido expresiones cada vez más utilizadas por profesionales e investigadores. Sin embargo, estos conceptos todavía están mal definidos. Hegemónicamente, la clínica ha estado representada por investigaciones y actividades terapéuticas sobre las repercusiones de la enfermedad en el cuerpo, que está marcada por signos y síntomas. Sin embargo, en este sentido, con un enfoque en la enfermedad, la clínica hace poco para resolver los problemas de salud. Conclusion: Se propone que Clinical Care representa un nuevo campo conceptual, con el establecimiento de nuevas relaciones entre los sujetos, que se detiene en los signos y síntomas, pero también en las repercusiones psicológicas y sociales de la enfermedad, considerando a la persona centralmente y no a la persona. enfermedad y su contexto de vida.
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