Personal Healthcare Systems Research Articles

Health care system barriers and facilitators to early prenatal care among diverse women in Florida.

Worldwide, women continue to experience barriers to accessing early prenatal care. Given the unique nature of the United States health care system, this study aimed to identify barriers and facilitators to early prenatal care reported by women in Florida. A mixed-methods study of postpartum women with entry to care after 14weeks' gestation was conducted at a tertiary care hospital in Florida. First, eligible women completed a survey including demographic characteristics and open-ended questions on prenatal care barriers. Second, a semi-structured interview was conducted to contextualize the conditions of access. The qualitative analysis was based on the combined theoretical frameworks of Phillippi and Khan. The majority of the 55 participants self-identified as Hispanic (n=28, 51%), non-Hispanic White (n=12, 22%), or non-Hispanic Black (n=9, 16%). Quantitative analysis identified frequent barriers, including the following: "couldn't get an appointment when wanted one" (n=24, 47%); "didn't have insurance" (n=21, 41%); and "not aware of pregnancy" (n=21, 41%). From the combined quantitative and qualitative analyses, three major themes emerged that help to explain barriers faced by birthing people in Florida: personal factors (mental health, awareness of pregnancy, considering abortion, tumultuous life), community conditions (transportation, stigma/fear, social support), and health care system (language barriers, delay at clinic level, cost of care). Barriers to early prenatal care are multifaceted and operate at personal, societal, and health care systems levels. Prenatal care practitioners have multiple potential targets for interventions at the clinic level to help mitigate barriers to early prenatal care. Strategies should include health care policy reforms to Medicaid access and interventions at the clinic level aimed at fast-tracking access to care.

Oral health as predictors of family functioning: A cross-cultural adaptation and validation of Hindi version of family impact scale – A cross-sectional study

Abstract Background The effect of individuals oral disease patterns on the family has been too little explored. Despite awareness of the importance of the family unit for personal health and health care system, the family unit has been infrequently studied from the public health point of view. Objective The specific aim of the study was to evaluate children's oral health as predictors of family functioning and to validate psychometric properties of Hindi version of family impact scale among 12-15-year-olds. Material and methods A total of 225 parent-child pair participated in the study. A cross-cultural translation, adaptation and validation of Hindi version of family impact scale (FIS) was conducted. Test-retest reliability was assessed by the intraclass correlation coefficients (ICC) using a two-way mixed-effect model. Discriminant validity was verified by family impact scores among parent-child pair with and without oral diseases. Construct validity was corroborated by association between the scale scores with global indicators of oral health and overall well-being. Clinical examinations were conducted using modified WHO criteria (1997). Influence of independent variables on the FIS were assessed using regression analysis. Results Mean impact score was 25.16 ± 5.96 with almost non-existent floor effect (4%) and no ceiling effect in the population. Inter Class Correlation for FIS scale was 0.86 indicating perfect agreement. Parents of children with dental caries and poor oral hygiene reported higher overall FIS scores (p Conclusions This study provides evidence that oral disorders have a significant impact on family functioning. The Hindi version of the FIS exhibited good psychometric properties, with acceptable validity and reliability. Future research needs to explore the consequences of higher family impact scores and focus on strategies to reduce the impacts of oral conditions on the family.

Internet of Things as a Logical Way to Tackle the Emerging COVID-19 Virus

The outburst of thenovel coronavirus (COVID-19) that hasledto millionsof deathsglobally appears tobe overwhelming. This virushas become a focus of worldwide consideration. The Internet of Things (IoT) can be well-thought-out as a practical way to forecast, monitor, and avert the emerging COVID-19 virus. Internet of Things represents a connected-network of smart objects, sensors, devices, and people over which information can be acquired and communicated via the cloud for trend and patterns analysis. This technology has gained immense research plethora in health care and industrial applications. The IoT revolution is reforming contemporary healthcare schemes through integrating high-tech, commercial, and societal views. This paper briefly examines the potential of IoT technology as a logical way to tackle COVID-19 by evolving healthcare systems from conventional to more personalized healthcare systems through whichillness can be detected, treated, and supervisedeffortlessly

Welfare Genome Project: A Participatory Korean Personal Genome Project With Free Health Check-Up and Genetic Report Followed by Counseling.

The Welfare Genome Project (WGP) provided 1,000 healthy Korean volunteers with detailed genetic and health reports to test the social perception of integrating personal genetic and healthcare data at a large-scale. WGP was launched in 2016 in the Ulsan Metropolitan City as the first large-scale genome project with public participation in Korea. The project produced a set of genetic materials, genotype information, clinical data, and lifestyle survey answers from participants aged 20–96. As compensation, the participants received a free general health check-up on 110 clinical traits, accompanied by a genetic report of their genotypes followed by genetic counseling. In a follow-up survey, 91.0% of the participants indicated that their genetic reports motivated them to improve their health. Overall, WGP expanded not only the general awareness of genomics, DNA sequencing technologies, bioinformatics, and bioethics regulations among all the parties involved, but also the general public’s understanding of how genome projects can indirectly benefit their health and lifestyle management. WGP established a data construction framework for not only scientific research but also the welfare of participants. In the future, the WGP framework can help lay the groundwork for a new personalized healthcare system that is seamlessly integrated with existing public medical infrastructure.

A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services.

This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication. The framework uses a fusion of electrocardiogram (ECG) and photoplethysmogram (PPG) signals when performing authentication. In addition to relying on the unique identification characteristics of the users’ biometric traits, the security of the framework is empowered by the use of Homomorphic Encryption (HE). The use of HE allows patients’ data to stay encrypted when being processed or analyzed in the cloud. Thus, providing not only a fast and reliable authentication mechanism, but also closing the door to many traditional security attacks. The framework’s performance was evaluated and validated using a machine learning (ML) model that tested the framework using a dataset of 25 users in seating positions. Compared to using just ECG or PPG signals, the results of using the proposed fused-based biometric framework showed that it was successful in identifying and authenticating all 25 users with 100% accuracy. Hence, offering some significant improvements to the overall security and privacy of personalized healthcare systems.

An Improved Algorithm to Enhance the Performance of FAST TCP Congestion Control for Personalized Healthcare Systems

The development of personalized medical systems should be supported by a fast and stable network system. The FAST TCP network system is the appropriate support system for this purpose. However, when the FAST TCP is deployed, the static mapping selection method for protocol parameters is unable to guarantee the small queuing delay and fast convergence of the network simultaneously. By conducting theoretical analysis and simulation experiments, the relationships among FAST TCP protocol slow start condition, control law gain parameters, and FAST TCP system convergence rate were examined. To ensure the stability of the FAST TCP system and to select the smallest protocol parameters, an improved method to effectively accelerate the convergence velocity of the FAST TCP system is proposed in this study. In this method, the number of packets for staying in the buffer for FAST TCP connections was taken as the criterion of the slow start, and the gain parameter of the control law was dynamically adjusted according to the local information of each FAST TCP connection. Using this improved method, the FAST TCP system can achieve a stable and small queuing delay, whilst the FAST TCP system could converge quickly to the equilibrium point simultaneously.

Highly‐Sensitive Skin‐Attachable Eye‐Movement Sensor Using Flexible Nonhazardous Piezoelectric Thin Film

AbstractAccurate and continuous monitoring of eye movements using compact, low‐power‐consuming, and easily‐wearable sensors is necessary in personal and public health and safety, selected medical diagnosis techniques (point‐of‐care diagnostics), and personal entertainment systems. In this study, a highly sensitive, noninvasive, and skin‐attachable sensor made of a stable flexible piezoelectric thin film that is also free of hazardous elements to overcome the limitations of current computer‐vision‐based eye‐tracking systems and piezoelectric strain sensors is developed. The sensor fabricated from single‐crystalline III‐N thin film by a layer‐transfer technique is highly sensitive and can detect subtle movements of the eye. The flexible eye movement sensor converts the mechanical deformation (skin deflection by eye blinking and eyeball motion) with various frequencies and levels into electrical outputs. The sensor can detect abnormal eye flickering and conditions caused by fatigue and drowsiness, including overlong closure, hasty eye blinking, and half‐closed eyes. The abnormal eyeball motions, which may be the sign of several brain‐related diseases, can also be measured, as the sensor generates discernable output voltages from the direction of eyeball movements. This study provides a practical solution for continuous sensing of human eye blinking and eyeball motion as a critical part of personal healthcare, safety, and entertainment systems.

Internet of Things for Current COVID-19 and Future Pandemics: an Exploratory Study.

In recent years, the Internet of Things (IoT) has gained convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. At the time this paper was written, the number of diagnosed COVID-19 cases around the world had reached more than 31 million. Since the pandemic started, there has been a rapid effort in different research communities to exploit a wide variety of technologies to combat this worldwide threat, and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT-enabled/linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patient recovery. This paper surveys the role of IoT-based technologies in COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases, including early diagnosis, quarantine time, and after recovery.

Ethical interaction of three elements of medicine during COVID-19

The humanitarian idea underlying this article is to attempt an epidemiological interpretation of the classic Hippocratic triad "Medicine consists of three elements: the disease, the patient and the doctor". In the XIII century, the Syrian doctor Abul-Faraj in his saying: "Look, there are three of us - you, me, and the disease. If you are on my side, it will be easier for the two of us to defeat her. But, if you go over to her side, I alone will not be able to defeat you both" deciphered the magical meaning of these words. For centuries, the fundamental integrity of this formula has been an ethical and professional guarantee of the success of each patient's treatment and the prospect of building a personalized healthcare system. In this particular article, we have searched for new content of three key elements of the textbook aphorism in the context of the COVID-19 pandemic. An understanding of the role of the doctor - "I" as the whole complex of efforts aimed at fighting the pandemic. Patient status "You" means the whole society during a pandemic, and even is as a long-term message for the physical, mental, social and geopolitical health of future generations. The meaning of "Disease" should be understood from the perspective of the problems of the entire health system and logistical ignorance, which has become an obstacle to achieving ethical integrity in managing epidemic challenges. The paper shows how adherence to the ethical principles of social responsibility, trust, and solidarity should become the moral accompaniment of the entire complex of sanitary, anti-epidemic, economic, legal, and social technologies that can ensure success in the fight against the pandemic and prevent the development of unjustified risks.

Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study.

BackgroundSmartphone-based blood pressure (BP) monitoring using photoplethysmography (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension.ObjectiveThis study aimed to develop a mobile personal health care system for noninvasive, pervasive, and continuous estimation of BP level and variability, which is user friendly for elderly people.MethodsThe proposed approach was integrated by a self-designed cuffless, calibration-free, wireless, and wearable PPG-only sensor and a native purposely designed smartphone app using multilayer perceptron machine learning techniques from raw signals. We performed a development and usability study with three older adults (mean age 61.3 years, SD 1.5 years; 66% women) to test the usability and accuracy of the smartphone-based BP monitor.ResultsThe employed artificial neural network model had good average accuracy (>90%) and very strong correlation (>0.90) (P<.001) for predicting the reference BP values of our validation sample (n=150). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. However, according to the Association for the Advancement of Medical Instrumentation and British Hypertension Society standards, only diastolic blood pressure prediction met the clinically accepted accuracy thresholds.ConclusionsWith further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of health care, particularly in rural zones, areas lacking physicians, and areas with solitary elderly populations.

Ultrastretchable, Self‐Healable, and Wearable Epidermal Sensors Based on Ultralong Ag Nanowires Composited Binary‐Networked Hydrogels

AbstractStretchable and biocompatible flexible electronic devices are essential to meet the increasing demands of complex and multifunctional personal healthcare systems. To detect various external stimuli, noninvasively epidermal sensors with reliable and sustainable performances are desirable. Herein, ultrastretchable, self‐healable, and wearable epidermal sensors based on ultralong Ag nanowires (AgNWs) composited binary‐networked hydrogels are fabricated. The flexible hydrogel sensors can monitor dynamic strains in a wide range (4–3000%), realize high healing efficiency (94.3%) and strong adhesiveness, which is attributed to the strong covalent bond and reversible physical interaction structured binary‐network. The ultralong AgNWs network remains in direct contact under strain, ensures a rapid response to external stimuli. The strong interactions between polymer matrix and the nanowires endow the hydrogel sensors excellent sensitivity (gauge factor of 4.59) within a wide sensing range (0–850%). The cycling stability of the hydrogel sensors is further improved by the composition of AgNWs, presenting negligible degradation both on tension and compression. Based on the advantageous performances, the flexible stain sensors can differentiate complicated human motions and realize phonation recognition precisely, showing promising application in next‐generation wearable epidermal sensors with ultrabroad working range and high sensitivity.

Theoretical study of superlubric nanogenerators with superb performances

Nanogenerators promise self-powered sensors and devices for extensive applications in internet of things, sensor networks, big data, personal healthcare systems, artificial intelligence et al. However, low electric current densities and short product lifespans have blocked nanogenerators' applications. Here we show that structural superlubricity, a state of nearly zero friction and wear between two contacted solid surfaces, provides a revolutionary solution to the above challenge. We propose the first three types of superlubric nanogenerators (SLNGs), namely the capacitor-based, triboelectric, and electret-based SLNGs. With a systematical analysis on the influences of material and structural parameters to these SLNGs’ performances, we demonstrate that these SLNGs can achieve not only enduring lifespans, but also superb performances – three orders of magnitude in current densities and output powers higher than those of conventional nanogenerators. Furthermore, we show that SLNGs can be driven by very weak external loads (down to ~1 μN) in very low frequencies (down to ~1μHz), and are thus capable to harvest electric energies from an extremely board spectrum of environments and biosystems. Among the three types of SLNGs, the capacitor-based is synthetically most competitive in the senses of performance, fabrication and maintaining. These theoretical results can guide designs and accelerate fabrications of SLNGs toward real applications.

Radiology, Mobile Devices, and Internet of Things (IoT).

Radiology by its nature is intricately connected to the Internet and is at the forefront of technology in medicine. The past few years have seen a dramatic rise in Internet-based technology in healthcare, with imaging as a core application. Numerous Internet-based applications and technologies have made forays into medicine, and for radiology it is more seamless than in other clinical specialties. Many applications in the practice of radiology are Internet based and more applications are being added every day. Introduction of mobile devices and their integration into imaging workflow has reinforced the role played by the Internet in radiology. Due to the rapid proliferation of wearable devices and smartphones, IoT-enabled technology is evolving healthcare from conventional hub-based systems to more personalized healthcare systems. This article briefly discusses how the IoT plays a useful role in daily imaging workflow and current and potential future applications, how mobile devices can be integrated into radiology workflows, and the impact of the IoT on resident and medical student education, research, and patient engagement in radiology.

COVID-19 What Have We Learned? The Rise of Social Machines and Connected Devices in Pandemic Management Following the Concepts of Predictive, Preventive and Personalised Medicine

Objectives: Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalised interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions, has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalised interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis.Method: This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles, and other reliable data sources.Results: Most countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning, and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalised solutions for pandemic management, based on social machines and connected devices.Solutions: The proposed predictive, preventive and personalised solutions are based on the integration of IoT data, wearables devices data, mobile apps data, and individual data inputs from registered users, operating as a social machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the social aspect of human-computer interactions (social machines) and the increased connectivity of humans and devices (internet of things).Conclusion: Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalisation of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalised digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

Automated myocardial infarction identification based on interbeat variability analysis of the photoplethysmographic data

Background and objectiveMyocardial infarction (MI) remains a major cause of mortality around the world for decades. Timely detection followed by instant medical intervention is strongly recommended to minimize MI related death threats. Generally, electrocardiogram (ECG) based automated techniques are preferred to ensure early diagnosis of MI. Recently, Photoplethysmogram (PPG) signal is evolving as a promising diagnostic tool for several cardiac monitoring applications because of its low cost, reliable and easy acquisition technology. However, the use of PPG for MI detection has not been much explored till date. Hence, in the present work, a method for MI detection is proposed based on the use of the PPG signal only. MethodThe pathophysiological alteration due to MI induces a beat-to-beat variation in the PPG beat morphology. Different time-plane parameters from the PPG signal and its derivatives are extracted to represent the individual beat morphologies and their variations are then analyzed as features over the cardiac cycles. By using different feature optimization techniques, finally five features are selected that presents discriminating variability to identify MI. ResultsThe proposed method is evaluated with PPG records collected from 52 hospitalized MI patients and 52 normal subjects. The optimized five feature set, representing the inter beat morphological variations exhibits significant performance along with SVM (linear) classification technique with an average sensitivity, positive predictivity and detection accuracy of 92.70 %, 98.10 %, and 95.40 %, respectively. ConclusionsCompared to other related works, use of PPG signal for MI detection is studied in this research for the first time. The promising result obtained establishes the utility of PPG signal for MI detection with a potential of implementation in the personal healthcare systems.

Toward Smart Treatment Management for Personalized Healthcare

A remarkable increase of personalized healthcare services has been powered by the broad implementation of the networks and their ramifications. The core value of using personalized healthcare systems is to create customized medical service offerings and establish interconnections between patients and physicians. However, the treatment delivery for individual patients is encountering a challenge in effectively creating plans in which medical services can be retrieved from multiple sources at various costs. This article focuses on the problem of minimizing total cost of service retrieval and proposes an approach that uses intelligent agents to dynamically make service retrieval strategies. The approach is called the Smart Treatment for Personalized Healthcare (STPH) model, which is designed to produce an optimal solution to minimizing total cost on the intelligent agent. The performance of the proposed approach has been validated by experimental evaluations.

An intelligent healthcare system with data priority based on multi vital biosignals

Background and Objective: Home-based personal healthcare systems are becoming popular and affordable due to the development of Internet of Things (IoT) devices. However, with an increasing number of users, such healthcare systems are challenged to store and process enormous volumes of data. For instance, multi-biosignal data are collected continuously from patients using IoT device like body sensors and are sent to the server by portable devices for further analysis (e.g., knowledge discovery or the clinical event prediction). These enormous amount of data from large number of patients are causing the transmission overhead and high latency in the network which are responsible for inefficiency issues in clinical event prediction. To address these problems, in this paper, data assessment method is introduced to improve the efficiency in data collection and data prediction.Methods: The assessment algorithm is inspired by National Early Warning Score (NEWS) used in Emergency Department. In our method, only the abnormal time-sequence data for analysis are sent to the server. Thus, the waiting time of data before prediction can be optimized because data with higher priority are processed in front of those with lower priority, which helps our system to provide diagnostic decisions in a proper time according to patients’ urgency.Results: Our experiments show that the proposed model ideally can save 20% volume of data in the collection and can reduce 75% waiting time of data with the highest priority before predicting. In addition, the waiting time of data for further analysis is optimized compared to the normal processing flow.Conclusion: The paper introduces an enhanced healthcare system with assessing data priority in order to optimize the data collection and the prediction in terms of data size and waiting time.

The convention on the rights of persons with disabilities versus the right to health care

By employing the research methodology of thematic analysis, the paper presents the investigation of the Convention on the Rights or Persons with Disabilities in the aspect of the right to health care. The revealing of fundamental resolutions of the Convention on the Rights of Persons with Disabilities, manifestation of them in the aspect of the health care system are the object of the present paper; moreover, these emphasise the social relevance seeking to ensure comprehensive and equal use of all human rights and fundamental freedoms, dignity, full participation in public life and, naturally, health care system for persons with disabilities. The research problem is formulated within this context: What resolutions of the Convention on the Rights of Persons with Disabilities are the most important to successfully ensure persons’ with disabilities rights to health care? What is the semantic manifestation of these resolutions and what are the contexts that ensure successful implementation of them?

Electroactive properties of electrospun silk fibroin for energy harvesting applications

Silk fibroin (SF) is a sustainable material explored with success in the textile and biomedical industries. This protein has outstanding mechanical and electrical properties that could be used for advanced technological applications. In this work, silk fibroin fibrous membranes were processed by electrospinning, with conditions obtained through orthogonal experimental design. Due to their high-water affinity, the membranes were stabilized against aqueous environments by exposure to methanol (MeOH) in vapor phase. Individual SF fibres present an apparent piezoelectric constant (d33) of 38 ± 2 pm/V for the as-spun ones and 28 ± 3 pm/V for those submitted to MeOH in vapor phase, a piezoelectric voltage coefficient (g33) of 1.05 and 0.53 Vm.N−1, respectively, a mechano-sensibility of 0.15 V kPa−1, an energy storage capacity of 85 μJ, and an efficiency up to 21%. When the SF is submitted to MeOH in vapor phase, no effect was observed in the protein secondary structures. This work represents a new insight into the origin of the piezoelectric properties of SF and opens new opportunities for applications in microelectronics engineering for self-powered epidermal electronics, implantable medical devices, and personalized health care systems.

A wireless body area sensor network based on stretchable passive tags

A body area sensor network (bodyNET) is a collection of networked sensors that can be used to monitor human physiological signals. For its application in next-generation personalized healthcare systems, seamless hybridization of stretchable on-skin sensors and rigid silicon readout circuits is required. Here, we report a bodyNET composed of chip-free and battery-free stretchable on-skin sensor tags that are wirelessly linked to flexible readout circuits attached to textiles. Our design offers a conformal skin-mimicking interface by removing all direct contacts between rigid components and the human body. Therefore, this design addresses the mechanical incompatibility issue between soft on-skin devices and rigid high-performance silicon electronics. Additionally, we introduce an unconventional radiofrequency identification technology where wireless sensors are deliberately detuned to increase the tolerance of strain-induced changes in electronic properties. Finally, we show that our soft bodyNET system can be used to simultaneously and continuously analyse a person’s pulse, breath and body movement. By integrating wireless stretchable on-skin sensor tags and flexible readout circuits attached to textiles using an unconventional radiofrequency identification design, a body area sensor network can be created that can continuously analyse a person’s pulse, breathing and body movement.