Use In Healthcare Applications Research Articles

IoT devices in use in healthcare applications: A systematic literature review

IoT devices in use in healthcare applications: A systematic literature review

Analysis of Factors Influencing We-Intention in Healthcare Applications Based on the AISAS Model

The purpose of this study is to develop and test a theoretical model that can be used to predict the influencing factors affecting users’ use of healthcare applications. Our research structure model is based on the Attention-Interest-Search-Action-Share (AISAS) model, combined with the indicators of personal intention and social identity, to predict we-intention. The proposed model was tested through a survey of 353 users of healthcare applications from mainland China and Taiwan. Based on the five characteristics of the AISAS model, this study integrates three levels of influencing factors (individual dimension, social dimension, and application design dimension) that affect users’ use of healthcare applications. It also verifies that users have both personal intention and we-intention when using healthcare applications. This research contributes to the theoretical development of the AISAS model, we-intention and healthcare applications. Finally, implications for theory and practice and directions for future research are discussed.

Software‐defined networks‐enabled fog computing for IoT‐based healthcare: Security, challenges and opportunities

AbstractThe Internet of Things (IoT) is a crucial technology in the healthcare industry thanks to recent developments in automated data collection. Current statistics of the healthcare sector show that it is expanding at a remarkable rate. However, the performance of the healthcare system is impacted by many challenges, such as security, privacy, latency, scalability, and heterogeneity that come with advancement. Fog computing and software‐defined networks (SDN) are critical enabling technologies that help to improve IoT‐based healthcare systems by ensuring timely and reliable data provisioning for use in healthcare applications. SDN solves interoperability, device management, and network management issues and automates patient health monitoring without human interaction. SDN and fog computing in IoT‐based healthcare can optimize device communication and computing power at a low cost. SDN‐enabled fog computing has lower latency and better security than cloud‐based IoT healthcare architecture. The researchers have not entirely discovered SDN‐enabled fog computing to the best of our knowledge. This paper covers healthcare IoT technicalities and related technologies. We examined ways to merge SDN, fog computing, and IoT to improve healthcare solutions. Finally, we concluded the paper with research issues and future healthcare system improvements.

Multi-disease prediction with machine learning

In the present era, Machine learning (ML) algorithms are extensively used in computer assisted diagnosis of the disease based on the symptoms of the disease. The widespread use of healthcare applications in the pandemic time, provides a motivation to further develop new computer assisted diagnostic application in the healthcare domain. Prevention and treatment of disease, accurate and timely diagnosis of any health-related problem is essential. In the case of a serious illness, a standard diagnostic method may not be enough. We have proposed a system for predicting the disease. There were about forty-one diseases in the data corpus that needed to be analyzed based on the symptoms. The system delivers a disease prediction that a person may have depending on the symptoms. This diagnostic program can assist a physician in diagnosing disease, allowing for timely treatment and saving lives. The disease forecasting system was developed using ML models such as the Random Forests, the Naive Bayes, and the Support Vector Machine Classification Algorithm. The presented work outlines an analysis of the aforementioned algorithms.

Optical fibre sensor for simultaneous temperature and relative humidity measurement: Towards absolute humidity evaluation

Temperature and humidity are essential parameters in monitoring the health of patients in critical care. An optical fibre sensor has been developed for simultaneous measurement of relative humidity (RH) and temperature at a single optical fibre tip based on the reflected intensity. Combining these measurements enables absolute humidity values to be obtained. The fibre tip is first modified with a coating of poly(allylamine hydrochloride) (PAH) / silica nanoparticles (SiO2 NPs) for relative humidity (RH) measurement and then coated with thermochromic liquid crystal (TLC) for temperature measurement. Experimental results demonstrate that the RH and temperature sensitivity are respectively 0.43 %/RH% (intensity at a wavelength of 650 nm) from 55 to 90% RH (R2 = 0.973) and 3.97 nm/°C from 28 to 46 °C (R2>0.99). Moreover, the proposed sensor has low crosstalk between each of the sensing parameters, with a response time of 3.1 s temperature (30–38 °C) and 13.2 s for relative humidity (20–80 %). In comparison to grating based optical fibre sensors the proposed sensor is low-cost with a simple manufacturing process which has the potential to find widespread use in healthcare applications.

Differentiated Service Model-Supported Cluster-Based Routing in Wireless Sensor Networks

Wireless Sensor Network finds its extensive use in healthcare applications for the transfer of time-critical data through wireless connectivity. The primary cause of network failure is the transfer of time-critical multimedia data. The article presents a new differentiated service modelsupported (DSM) cluster-based routing in wireless sensor networks (WSNs) that overcomes the above issue. DSM prioritizes the transfer of different flow types based on packet type and packet size. The employment of computational offloading minimizes delay for critical and small-sized data packets and by carrying out data reduction of large-sized packets at proxy server. It outperforms the existing protocols in terms of energy efficiency, throughput, and reliability by prioritizing the transfer of time-critical health application data

Reliable Data Collection in Participatory Trials to Assess Digital Healthcare Applications

The number of digital healthcare mobile applications in the market is exponentially increasing owing to the development of mobile networks and widespread usage of smartphones. However, only few of these applications have been adequately validated. Like many mobile applications, in general, the use of healthcare applications is considered safe; thus, developers and end users can easily exchange them in the marketplace. However, existing platforms are unsuitable for collecting reliable data for evaluating the effectiveness of the applications. Moreover, these platforms reflect only the perspectives of developers and experts, and not of end users. For instance, typical clinical trial data collection methods are not appropriate for participant-driven assessment of healthcare applications because of their complexity and high cost. Thus, we identified the need for a participant-driven data collection platform for end users that is interpretable, systematic, and sustainable, as a first step to validate the effectiveness of the applications. To collect reliable data in the participatory trial format, we defined distinct stages for data preparation, storage, and sharing. The interpretable data preparation consists of a protocol database system and semantic feature retrieval method that allow a person without professional knowledge to create a protocol. The systematic data storage stage includes calculation of the collected data reliability weight. For sustainable data collection, we integrated a weight method and a future reward distribution function. We validated the methods through statistical tests involving 718 human participants. The results of a validation experiment demonstrate that the compared methods differ significantly and prove that the choice of an appropriate method is essential for reliable data collection, to facilitate effectiveness validation of digital healthcare applications. Furthermore, we created a Web-based system for our pilot platform to collect reliable data in an integrated pipeline. We compared the platform features using existing clinical and pragmatic trial data collection platforms.

Low-Cost Inkjet-Printed RFID Tag Antenna Design for Remote Healthcare Applications

This paper presents a low-cost, inkjet printed radio frequency identification (RFID) tag antenna for remote healthcare applications. The electrically small tag consists of nested-slot configuration and parallel strips. The tag antenna is exploited as a sensor by modifying its equivalent circuit to mitigate the effects of water, blood sample phantom, and the human body. As a result, the proposed RFID tag antenna with compact dimensions of 40 × 14 mm2, features a conjugate match with Impinj R6 RFID chip ranging from 890 to 937 MHz. Moreover, this tag has a read range of 3 m, 2.5 m, and 1.5 m on the water bottle, intravenous (IV) solution and blood bag, respectively. However, the read range of RFID tag on an empty water bottle or IV solution bag is 0.5 m. By comparing the read range of tag on empty and solution filled IV bags, the proposed tag was used as a water proximity sensor. Experimental testing of the tag is performed for sensing the level of the IV solution. Also, this tag is tested after mounting on the liquid mixture (a mixture of salt and sugar is used as a phantom to mimic the blood) filled plastic bags, which leads to a low-cost solution for blood storage management. Experimental results show a good agreement of the proposed tag toward its use in healthcare applications, which leads to better healthcare facilitation regarding cost, time, and care.

The Personalization of Conversational Agents in Health Care: Systematic Review.

BackgroundThe personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents.ObjectiveThe goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation.MethodsWe searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals; (2) involved a conversational agent with an unconstrained natural language interface; (3) tested the system with human subjects; and (4) implemented personalization features.ResultsThe search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common examples of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly.ConclusionsMost of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making.

Evaluating a South African mobile application for healthcare professionals to improve diagnosis and notification of pesticide poisonings

BackgroundMobile health is a fast-developing field. The use of mobile health applications by healthcare professionals (HCPs) globally has increased considerably. While several studies in high income countries have investigated the use of mobile applications by HCPs in clinical practice, few have been conducted in low- and middle-income countries. The University of Cape Town developed a pesticide notification guideline which has been adapted and embedded into a South African Essential Medical Guidance mobile application. This study evaluated the usefulness of the guideline within a mobile application for improving the ability of HCPs to diagnose and notify on acute pesticide poisonings (APPs).MethodsA descriptive online questionnaire, with 15 open- and 20 closed-ended questions, was completed by 50 South African emergency medicine physicians and registrars (i.e. medical doctors training as specialists) between December 2015 to February 2016. Descriptive statistics were used to calculate response frequencies and percentages using SPSS version 23. Texts from the open-ended questions were thematically analysed. Fisher’s exact test was applied to determine associations.ResultsA significant association was found between participants’ knowledge that APP is a notifiable condition, and ever reporting the poisoning to the National Department of Health (p = 0.005). Thirty four percent of the participants were aware of the guideline within the Essential Medical Guidance application despite only seven participants having used it. Those who used the guideline found it provided useful information for the identification of unlabelled pesticides products and promoted reporting these cases to the National Department of Health for surveillance purposes. In addition, it appeared to facilitate the prompt diagnosis and treatment of APP cases, and most intended to continue using it for training and educational purposes.ConclusionsMobile health applications appear to support overburdened medical education programmes and promote better patient care. However, since most participants were not aware of the existence of the pesticide guideline within the studied essential medicine application, there is potential for the use of healthcare applications to play a more central role in healthcare systems and medical training. Furthermore, the field of medical informatics could support HCPs through mobile applications in improving reporting of APP.

Imparting functionality viz color, antioxidant and antibacterial properties to develop multifunctional wool with Tectona grandis leaves extract using reflectance spectroscopy

In recent years, considerable attention has been given to plant colorants for use in dyeing applications because of their newly discovered properties such as insect repellency, deodorizing, antioxidant, sun protection, and antibacterial activity. In view of their better biocompatibility, biodegradability, less-toxicity, natural colorants are gaining worldwide interest for producing elegant hues and highly functional value-added textiles. In the present work, we investigated the coloring and functional finishing of wool with Tectona grandis L. leaves extract with the emphasis on effect of metallic salt mordants on colorimetric potential, fastness, radical scavenging potential, and antibacterial activity in an attempt to develop bioactive fibres for use in health care applications. The pretreatment of wool with ferrous sulfate, potassium aluminium sulfate and stannous chloride provided a significant enhancement in CIELa*b* parameters and fastness properties. Antioxidant tests showed that the control dyed wool had excellent radical scavenging activity than mordanted samples. In addition, metal mordants particularly ferrous sulfate enhanced the durability of antibacterial efficacy to several laundering cycles. It can be concluded that the extract of Tectona grandis may be developed as multifunctional finishing agent to impart simultaneous color, antioxidant and antibacterial properties to wool based fabrics.

Influential Factors of Smart Health Users according to Usage Experience and Intention to Use

As the use of smartphones and applications has increased, the use of healthcare applications also has increased. In the burgeoning applications market, research regarding customers' characteristics is necessary for the development of specific application products or services that fulfill customers' various needs. This study aimed to analyze the characteristics of and differences in the influential factors of healthcare application use among smartphone users. A survey was conducted in 300 adults from September 16 to October 15, 2011. The research subjects were categorized into three groups, depending on their usage experience and intention to use healthcare applications. We analyzed the characteristics of each group and the differences in influential factors among the groups. The healthcare application users were female, older than 30 years old, and more educated. The current customers had high levels of self-efficacy and innovativeness. Perceived usefulness negatively affected to actual use of healthcare applications, and both perceived ease of use and enjoyment positively influenced on intention to use. Therefore, developers and managers of healthcare applications should consider these characteristics of current and potential customers to improve adoption of healthcare applications.

Optimal beacon scheduling mechanisms using cluster identifier for healthcare application

This paper presents optimal beacon scheduling mechanisms using the cluster identifier (CID) for medical and healthcare applications. We consider the emerging low-rate wireless personal area networks (WPAN) technology as specified in the IEEE 802.15.4 standard that can support medical and healthcare applications. In addition, we have adopted the concept of cluster-tree human body networks (HBNs) for use in healthcare applications. A cluster-tree network conforming to the IEEE 802.15.4 standard is formed by several coordinators that periodically send beacon frames to the nodes of their cluster. If these periodic beacon frames are sent without any particular scheduling, they will collide with each other and the nodes that wait for the periodic beacon frames will loose synchronization with their coordinators. The purpose of this paper is to overcome this problem by proposing optimal beacon scheduling mechanisms using the CID for cluster-tree HBNs. These mechanisms are designed such that the PAN coordinator controls the beacon transmission time of all of the cluster heads (CHs) using the CID field located in the beacon frame of the PAN coordinator, in order to avoid beacon frame collisions. In particular, only those sensor nodes located in the cluster that needs the medical attention of the hospital staff according to the patients’ condition transmit their sensing data to the PAN coordinator via the CH, while those sensor nodes located in other clusters return to sleep mode. Accordingly, the lifetime of the sensor nodes is prolonged by using the cluster-tree HBNs model. We evaluated the performance of our scheme through a mathematical analysis and a series of simulations using the Network Simulator 2 (NS-2).