Personal Health Care Research Articles

The Study on the Problems of Digitally Disadvantaged Groups in Using Online Medical Registration Public Numbers--Take Changsha Xiangya Hospital as an Example

With the rapid progress of information technology, smart healthcare has been gradually integrated into people's daily lives. People pay more and more attention to their personal health care, and they are willing to invest more time and energy in health care, and start to transition from the original "medical treatment only when there is a disease" to the current "prevention of disease, intervention of disease, and focus on rehabilitation". At the same time, under the combination model of "Internet technology + hospital", patients can make an appointment with a doctor through the online form, which makes it easier and easier to seek medical advice. Changsha Xiangya Hospital, a famous modern medical hospital in Hunan Province, China, provides convenient services with its online medical registration public number, but also faces the challenge of the digitally disadvantaged group's difficulties in using it. The purpose of this paper is to explore in depth the problems encountered by this group in using the online medical registration public number and to propose corresponding optimization suggestions to promote the fairness and universality of medical services. This thesis finds that although the digitally disadvantaged groups is not able to use online medical registration flexibly, they have been presenting a positive learning attitude and are happy to learn about the process of using the online medical registration public number.

Stakeholder Perception's of Cybersecurity for Welfare Technology and Remote Care Devices.

In healthcare, there are various stakeholders who hold different understandings of technology. Cybersecurity risks may also be something these stakeholder have varying perceptions of. This papers explores how cybersecurity risks are understood by two key stakeholder groups in the Norwegian healthcare sector related to welfare technology and personal healthcare devices. Two stakeholder groups (healthcare workers and technology vendors) have been interviewed to gather data on this topic. Key findings highlight that there are differences in how risks are perceived, both in terms of likelihood and in consequence. We apply risk perception theory to analyze these findings and provide suggestions for further research within this topic.

Critical Review on the Contribution of Machine Learning to Health Science

Background: The field of machine learning in health science is evolving exponentially, with a focus on accelerating scientific discoveries, improving holistic well-being, and advancing personalized healthcare. Aim: In this same spirit, this critical review article aims to provide a comprehensive understanding of the role, challenges, opportunities, and ethical considerations of integrating machine learning into health science, with an emphasis on healthcare research and practice. Methods: To base its critiques on previous literature, the elucidative survey considered specific criteria, such as the significance and contribution of each source to the field, methodology or approach, and argument, as well as the use of evidence. Results: The study results indicate that machine learning holds great promise to improve evidence-based health science, but significant work is needed to ensure the technology is developed and deployed in a way that is trustworthy and ethical. Conclusion: In conclusion, the literature review presents a balanced assessment of the strengths, weaknesses, and notable features of the current state of machine learning in health science. The key takeaway point is that while machine learning has demonstrated significant potential to improve health science outcomes and strategic management, there are still important challenges, limitations, and research gaps that need to be addressed to facilitate widespread adoption and trust in these technologies.

Circular RNAs: a small piece in the heart failure puzzle.

Cardiovascular disease, specifically heart failure (HF), remains a significant concern in the realm of healthcare, necessitating the development of new treatments and biomarkers. The RNA family consists of various subgroups, including microRNAs, PIWI-interacting RNAs (piRAN) and long non-coding RNAs, which have shown potential in advancing personalized healthcare for HF patients. Recent research suggests that circular RNAs, a lesser-known subgroup of RNAs, may offer a novel set of targets and biomarkers for HF. This review will discuss the biogenesis of circular RNAs, their unique characteristics relevant to HF, their role in heart function, and their potential use as biomarkers in the bloodstream. Furthermore, future research directions in this field will be outlined. The stability of exosomal circRNAs makes them suitable as biomarkers, pathogenic regulators, and potential treatments for cardiovascular diseases such as atherosclerosis, acute coronary syndrome, ischemia/reperfusion injury, HF, and peripheral artery disease. Herein, we summarized the role of circular RNAs and their exosomal forms in HF diseases.

3D printed electret as self-powered wearable sensor and futuristic implementation for on-spot bone injury

The importance of wearable pressure sensors extends to the progress of modern society and personal healthcare. The utility of currently available self-powered sensors is restricted in complex areas due to their limited geometrical configurations. In this study, an electret composed of polylactic acid (PLA) has been developed by 3D printing technology, where the pressure sensors with the desired complicated structure are possible to design. We demonstrate that the 3D-printed electret-based pressure sensor (3D-EPS) exhibits outstanding sensitivity (∼178 mV/kPa) with ultrafast response time (6 ms). It enables the capture of the biomechanical signals from different parts of bone-joint motions, which could be utterly suitable for monitoring injury recovery. We show that if the electret structure is printed as a shoe sole shape, then it generates 65 V of open circuit voltage and 12 μA of short circuit current during the human running action, indicating to use as an effective wearable energy harvester. It is also capable of detecting different motion and foot pressure distributions, which makes it a potential candidate as a wearable biomedical sensor. To demonstrate its futuristic potential applications in patient-specific bone damage recovery monitoring, the feasibilities of electrets with commonly desired structures have been shown to print.

Data-Fusion-Based Quality Enhancement for HR Measurements Collected by Wearable Sensors.

The advancements of Internet of Things (IoT) technologies have enabled the implementation of smart and wearable sensors, which can be employed to provide older adults with affordable and accessible continuous biophysiological status monitoring. The quality of such monitoring data, however, is unsatisfactory due to excessive noise induced by various disturbances, such as motion artifacts. Existing methods take advantage of summary statistics, such as mean or median values, for denoising, without taking into account the biophysiological patterns embedded in data. In this research, a functional data analysis modeling method was proposed to enhance the data quality by learning individual subjects' diurnal heart rate (HR) patterns from historical data, which were further improved by fusing newly collected data. This proposed data-fusion approach was developed based on a Bayesian inference framework. Its effectiveness was demonstrated in an HR analysis from a prospective study involving older adults residing in assisted living or home settings. The results indicate that it is imperative to conduct personalized healthcare by estimating individualized HR patterns. Furthermore, the proposed calibration method provides a more accurate (smaller mean errors) and more precise (smaller error standard deviations) HR estimation than raw HR and conventional methods, such as the mean.

A self-driven multifunctional microfluidic sweat analysis system for efficient sweat collection and real-time monitoring

Wearable sweat sensors offer the opportunity for non-invasive and real-time monitoring of metabolite information in sweat, enabling individuals to dynamically monitor their physiology states at the molecular level. However, sweat possesses characteristics such as small volume, volatility, susceptibility to epidermal contamination, and low analyte concentrations, making its reliable collection and detection challenge for wearable sweat sensors. In this work, we develop a self-driven multifunctional microfluidic sweat analysis system (SMMSAS) for efficient sweat collection and detection. The SMMSAS features with a wetting contrast for the sweat, which comprises superhydrophobic inlet, superhydrophilic microchannel enriched with nanofiber, and an absorbent test paper. Such a feature enables the spontaneous sweat transports in the form of dropwise due to the exertion of “Laplace force-capillary force-capillary force” on sweat, thereby facilitating the real-time sweat detection. SMMSAS integrated with the functions of impedance and colorimetry analysis is able to simultaneously record the multiple information in sweat, including sweat volume, sweating rate, total electrolyte concentration, glucose concentration, and pH value, all of which are important to reflect the physiological state of the human body. We also demonstrate the capability of SMMSAS in real-time monitoring the sweating conditions of the human body, which is attributed to its fast responsiveness (∼ 100 ms) for the wide range sweating rate (0.5−15 μL/min) and various total electrolyte concentration (0−80 mM), paving the way for next-generation personalized healthcare.

Romania's Pioneering Law: Establishing the Right to Personalized Medicine.

This analysis and commentary discuss Romania's landmark law, the first globally, acknowledging the right of citizens and patients to personalized medicine. Initiated following the EU Council's 2015 policy on personalized medicine, the law is a result of intersectoral collaborative efforts led by the Centre for Innovation in Medicine in Romania using a quadruple (later evolved to penta) helix model involving academia, public, private, and civil society sectors. Promulgated on May 24, 2023, the law legally entitles patients to personalized health care and in ways informed by individual genetic and phenotypic consideration. The law mandates informed consent for medical interventions and ensures data protection in accordance with the General Data Protection Regulation. We suggest that this pioneering legislation paves the way for integrating personalized medicine into Romania's health care system, shaping clinical practice, research, and health policy. In all, it marks a significant step in redefining health care delivery, emphasizing individualized treatment and the political determinants of personalized medicine, and setting a precedent for future health care innovations worldwide.

Hierarchical conducting networks constructed as resistive strain sensors for personal healthcare monitoring and robotic arm control

Flexible strain sensors are strongly demanded in fields of personal healthcare monitoring and human–machine interface, as they can accurately perceive external action to produce electrical output signals. Aiming at the existing problems, such as the balanced sensitivity and sensing range, durability as well as expanded application, this paper proposes a flexible strain sensor prepared based on electrospun thermoplastic polyurethane (TPU) membrane, followed by anchoring mixed conductive materials (carbon black (CB)/carbon nanotubes (CNT)) on its surface via an effective ultrasonic-assisted method. The resulting CB/CNT@TPU strain sensor delivers a desirable integration of sensing performances: good sensitivity, wide work range, fast response speed (<150 ms) and reliable cyclic stability (5000 cycles). Ascribed to the sensing behavior and additional breathability, strain sensor can be applied well to monitor human health, including human joints movements (fingers and wrists) and physiological signals (pulse and laryngeal vibration recognition). Finally, a multiaxial robotic arm control system is constructed, which provides a feasible strategy for intelligent robotic arms.

Living with Atypical Hemolytic Uremic Syndrome in the Netherlands: Patient and Family Perspective

IntroductionAtypical hemolytic uremic syndrome (aHUS) poses a significant health challenge due to its rarity and severity within the spectrum of thrombotic microangiopathy (TMA). Despite efforts to optimize and personalize health care for aHUS patients, understanding the individual experiences, needs and desires of aHUS patients and relatives remains limited. MethodsHere we present a nationwide, exploratory, qualitative interview study with a direct content analysis approach. In-depth interviews and a six-week evaluation were audio-recorded and conducted using a semi-structured topic guide, based on the Institute for Positive Health (IPH) model. ResultsAnalysis of ten interviews involving six aHUS patients and thirteen relatives revealed the prevalence of long-term disease symptoms in adult patients, notably fatigue, which significantly impacted daily functioning. Moreover, the resilience demonstrated by patients and their relatives was noteworthy, yet the acute phase of aHUS and the unpredictable nature of disease recurrence could profoundly affect mental well-being. The emotional toll of aHUS is pervasive, with feelings of fear, guilt and trauma persisting across disease phases in both patients and relatives. Challenges in medical care, including delays in diagnosis and the need for personalized and uniform protocols, were highlighted. Support was deemed crucial, indicating the necessity for enhancements in the accessibility of comprehensible disease information and psychological counselling. At last, complexities surrounding genetic testing and carriership were discussed. ConclusionThis study underscores the profound, enduring and multifaced impact of aHUS. The insights gleaned from the experiences and needs of aHUS patients and their relatives could lay the foundation for development and implementation of more personalized innovations in aHUS healthcare.

Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis.

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.

Metodología deductiva del cuidado: descripción y prueba de los modos de investigación acerca del cuidado

ObjectiveDefine the modes of procedure of the Deductive Care Methodology (DCM) in the generation of knowledge about person's health care. MethodologyDesign and test of the DCM modes based on three phases: mapping of the MDC, generation of models from this methodology and testing of the models through studies in a clinical context. ResultsThe MDC presents five levels of abstraction with three modes broken down to 16 types. The modes are: Philosophical Mode to conceptualize and obtain generalities about reality, Mathematical Mode to operate with generalities, and Physical Mode to operationally verify, validating the results and the predictive capacity of the model. This MDC allows the creation of three models: Knowledge Model about Person Care, an ontology of care, Vulnerability Model about the person and Taxonomic Triangulation Model for knowledge management. All models generate products for computational knowledge management. In addition, the models are applied in teaching and generate research with more than a hundred participations in conferences and journals, of which five impact publications (from 2008 to 2022) classified in the categories of Nursing and Informatics are analysed. ConclusionsThe MDC collects prior knowledge to work with certainties, evidence and applying inferences that do not depend on the number of cases or inductive designs. This research presents a formal structure of the MDC with an interdisciplinary orientation between Health Sciences and Computer Sciences.

Composite Warp-Knitted Textile with Wet–Thermal Comfort, Support, and Antimicrobial Activity for Personal Long-Term Healthcare

Composite Warp-Knitted Textile with Wet–Thermal Comfort, Support, and Antimicrobial Activity for Personal Long-Term Healthcare

Literature Review: Opioid Crisis in South West, Nigeria

The opioid crisis in Southwest Nigeria has significantly increased, with socioeconomic factors such as lack of healthcare access, unemployment, and poverty exacerbating the problem. The review explores the interactions between biological variables, cutting-edge technologies, financial constraints, and environmental factors that contribute to drug abuse and substance use disorders. It also analyzes the major classes of opioids and distinguishes between medicinal and non-medical uses. The epidemic has negative impacts on personal well-being, healthcare infrastructure, and societal cohesion. The research suggests a multifaceted strategy, including interventions and policy implications. It suggests reducing poverty as a first step in treating the underlying causes, increasing access to mental health services, early detection and intervention programs, and public awareness initiatives to prevent disease. Medication-assisted treatment (MAT) is also emphasized as a recovery strategy. The evaluation advocates for policy changes, tighter guidelines for opioid prescriptions, and public awareness initiatives to inform people about opiate abuse risks and encourage cautious use.

Comparison of Transportation Use Among Older and Younger Persons Experiencing Homelessness

Regardless of age, transportation determines access to necessary services and supports, including food, healthcare, shelter, and social connection for persons experiencing homelessness (PEH). This study compared experiences and perceptions of transportation among PEH aged &lt;49 years old to PEH aged 50+ years. We conducted a secondary qualitative data analysis of semi-structured interviews with 18 PEH staying in emergency shelters in Salt Lake County, Utah. Using thematic analysis, we identified three points of comparison between older and younger PEH: 1) Modes of transportation used; 2) Reasons for using public transit; and 3) Perceptions of public transit. While there are some similarities across transportation modes used (e.g., public transit, dedicated homeless-services shuttle, walking), older PEH also reported using taxi services when it could be afforded or shared with others. In addition, older PEH more often discussed using public transit to get to healthcare appointments, while younger PEH reported needing transit to get to places of employment. Though both participant groups described the prohibitive costs of transit, OPEH recommended that to use transit most successfully there need to be places to sit and rest when walking, as well as more publicly available bathrooms and crosswalks for safety. This age-based comparison offers insight into ways to increase transportation equity and support PEH of all ages who similarly have unmet mobility needs.

Hospital Innovations for the Next Decade

Ultimately, healthcare innovation leads to improved clinical care, with new technology improving healthcare efficiency, effectiveness, quality, and affordability. With ever-increasing demand on health systems worldwide, the ultimate goal of healthcare innovation is to improve the ability to meet public and personal healthcare needs through the optimization of health systems' performance. In this article, we will discuss hospital-based innovation within the next decade that yields scalable solutions within the fields of preventative, treatment, and infection control healthcare innovation. Governments face tough choices since medical innovations hold promises and perils. These innovations occur across multiple dimensions, including core sciences, drug development, care delivery, and organizational and business models. In particular, medical technology-related innovations are blossoming, with medical technology patents more numerous and growing faster than pharmaceutical patents over the last decade. Despite this enormous investment in innovation and the magnitude of the opportunity for innovators to both do good and do well, all too many efforts fail, losing billions of investor resources along the way. [1] Barriers to disruptive innovation are often the public themselves acting through fear, enacting stringent regulation, supported by established professionals afraid to lose income and hospitals their investment in expensive systems. [2]

Vital Signs: Mammography Use and Association with Social Determinants of Health and Health-Related Social Needs Among Women - United States, 2022.

Approximately 40,000 U.S. women die from breast cancer each year. Mammography is recommended to screen for breast cancer and reduce breast cancer mortality. Adverse social determinants of heath (SDOH) and health-related social needs (HRSNs) (e.g., lack of transportation and social isolation) can be barriers to getting mammograms. Data from the 2022 Behavioral Risk Factor Surveillance System were analyzed to estimate the prevalence of mammography use within the previous 2 years among women aged 40-74 years by jurisdiction, age group, and sociodemographic factors. The association between mammography use and measures of SDOH and HRSNs was assessed for jurisdictions that administered the Social Determinants and Health Equity module. Among women aged 50-74 years, state-level mammography use ranged from 64.0% to 85.5%. Having health insurance and a personal health care provider were associated with having had a mammogram within the previous 2 years. Among women aged 50-74 years, mammography prevalence was 83.2% for those with no adverse SDOH and HRSNs and 65.7% for those with three or more adverse SDOH and HRSNs. Life dissatisfaction, feeling socially isolated, experiencing lost or reduced hours of employment, receiving food stamps, lacking reliable transportation, and reporting cost as a barrier for access to care were all strongly associated with not having had a mammogram within the previous 2 years. Identifying specific adverse SDOH and HRSNs that women experience and coordinating activities among health care providers, social services, community organizations, and public health programs to provide services that help address these needs might increase mammography use and ultimately decrease breast cancer deaths.

DNA-based molecular classifiers for the profiling of gene expression signatures

Although gene expression signatures offer tremendous potential in diseases diagnostic and prognostic, but massive gene expression signatures caused challenges for experimental detection and computational analysis in clinical setting. Here, we introduce a universal DNA-based molecular classifier for profiling gene expression signatures and generating immediate diagnostic outcomes. The molecular classifier begins with feature transformation, a modular and programmable strategy was used to capture relative relationships of low-concentration RNAs and convert them to general coding inputs. Then, competitive inhibition of the DNA catalytic reaction enables strict weight assignment for different inputs according to their importance, followed by summation, annihilation and reporting to accurately implement the mathematical model of the classifier. We validated the entire workflow by utilizing miRNA expression levels for the diagnosis of hepatocellular carcinoma (HCC) in clinical samples with an accuracy 85.7%. The results demonstrate the molecular classifier provides a universal solution to explore the correlation between gene expression patterns and disease diagnostics, monitoring, and prognosis, and supports personalized healthcare in primary care.

Associations between healthcare use and migration background in persons with dementia: A cohort study in the Netherlands

Background: Qualitative research shows barriers in access to healthcare for persons with a migration background, possibly leading to healthcare inequalities. We aimed to compare healthcare use between older community-dwelling persons with a migration background and native Dutch persons with dementia. Methods: Observational cohort study using electronic health records of NIVEL Primary Care Database (2013–2014) linked to nationwide administrative registries managed by Statistics Netherlands. We included all community-dwelling persons with dementia in the Netherlands aged 65 years and older. Negative binomial and logistic regression analyses were conducted to assess differences in healthcare use outcomes. The largest migrant groups (persons born in Surinam, Turkey and Morocco) were studied separately. Results: 138,864 persons with dementia were included in the study, whereof 3,991 persons with a migration background and 132,477 native Dutch persons. Moroccan-born persons showed 1.46 higher odds of having an unplanned hospital admission compared to native Dutch persons (95 % Confidence Interval [CI] 1.22–1.74). Persons with a migration background used less professional home care (Odds Ratio [OR] 0.55, 95 %CI 0.51–0.59), and received less home visits from their general practitioner (OR 0.50, 95 %CI 0.43–0.57) than native Dutch persons. Moroccan-born persons had more often an out-of-hours general practitioner consultation compared to native Dutch persons (Incidence Rate Ratio 1.36, 95 %CI 1.18–1.57). Conclusions: We found significant quantitative differences in healthcare use between persons with a migration background and native Dutch persons with dementia. Further research is needed to gain insight into the reasons of these differences, with consideration of specific migration background.

Deductive Care Methodology: Describing and testing modes of care research

ObjectiveDefine the modes of procedure of the Deductive Care Methodology (DCM) in the generation of knowledge about person's health care. MethodologyDesign and test of the DCM modes based on three phases: mapping of the DCM, generation of models from this methodology and testing of the models through studies in a clinical context. ResultsThe DCM presents five levels of abstraction with three modes broken down to 16 types. The modes are: Philosophical Mode to conceptualize and obtain generalities about reality, Mathematical Mode to operate with generalities, and Physical Mode to operationally verify, validating the results and the predictive capacity of the model. This MDC allows the creation of three models: Knowledge Model about Person Care, an ontology of care, Vulnerability Model about the person and Taxonomic Triangulation Model for knowledge management. All models generate products for computational knowledge management. In addition, the models are applied in teaching and generate research with more than a hundred participations in conferences and journals, of which five impact publications (from 2008 to 2022) classified in the categories of Nursing and Informatics are analysed. ConclusionsThe DCM collects prior knowledge to work with certainties, evidence and applying inferences that do not depend on the number of cases or inductive designs. This research presents a formal structure of the DCM with an interdisciplinary orientation between Health Sciences and Computer Sciences.