Chronic Patients’ Emotions toward Self-managing Care IT

Patients’ emotions toward health IT can play an important role in explaining their usage of it. One form of health IT is self-managing care IT, such as activity trackers that can be used by chronic patients to adopt a healthy lifestyle. The goal of this study is to understand the factors that influence the arousal of emotions in chronic patients while using these tools. Past studies, in general, tend to emphasize how IT shapes emotions, underplaying the role of the individual user’s identity and, specifically, how central health is to the user’s self in shaping emotions. In this research, the authors argue that patients’ health identity centrality (i.e., the extent to which they consider health as central to their sense of self) can play an important role in forming their dependence on health IT by affecting their use of it directly and shaping their emotions around it.

Comorbidity of Mental Disorder in Patients with Chronic Dis-ease: A Comparative Study of Heroin Dependence Patients and Diabetes Mellitus Patients

Background: Chronic patients are at greater risk for a psychiatric problem than the normal population; yet, the increased rate of mental disorder among one chronic patient compared to another chronic patient is uncertain. We aimed to assess the rate of mental disorder among people with heroin dependence and diabetes mellitus in comparison with the healthy population. Methods: This cross-sectional study was carried out in Kuala Lumpur, Malaysia in 2017-2020. The study consisted of 648 participants including heroin dependence patients, diabetes mellitus patients, and healthy population. The GHQ-28 and SCL-90-R scales were used to assess mental disorder among the study populations. Results: The current study revealed the rate of mental disorder among heroin dependence patients, diabetes mellitus patients, and healthy population respectively at 52.1%, 49.5%, and 23.2% using SCL-90-R and GHQ-28. The rate of mental disorder in both heroin dependent (OR 95%= 3.59: 2.37-5.44) and diabetic groups (OR 95%=3.25: 2.14-4.92) were significantly more than the healthy population; however, the odds ratio of mental disorder was not significantly different between heroin dependent and diabetic groups. Furthermore, the results revealed an acceptable agreement between SCL-90-R and GHQ-28 to detect mental disorders (Kappa=0.60; P<0.001). Conclusion: People with diabetes mellitus and heroin dependence have significantly poorer mental health than healthy people in Malaysia have. Furthermore, the equivalent rate of mental disorder among such patients suggests that heroin dependence patients are not more distressed than diabetes mellitus patients are. However, further comparative studies are needed to prove these findings.

Embedded AI-Based Digi-Healthcare

Healthcare is an indispensable part of human life and chronic illnesses like cardiovascular diseases (CVD) have a deeply negative impact on the healthcare sector. Since the ever-growing population of chronic patients cannot be managed at hospitals, therefore, there is an urgent need for periodic monitoring of vital parameters and apposite treatment of these patients. In this paper, an Internet of Medical Things (IoMT) -based remote patient monitoring system is proposed which is based on Artificial Intelligence (AI) and edge computing. The primary focus of this paper is to develop an embedded prototype that can be used for remote monitoring of cardiovascular patients. The system will continuously monitor physiological parameters like body temperature, heart rate, and blood oxygen saturation, and then report the health status to the authenticated users. The system employs edge computing to perform multiple functionalities including health status inference using a Machine Learning (ML) model which makes predictions on real-time data, alert notifications in case of an emergency, and transferring data between the sensor network and the cloud. A web-based application is developed for the depiction of raw data and ML results and to provide a direct communication channel between the patient and the doctor. The ML module achieved an accuracy of 96.26% on the test set using the K-Nearest Neighbors (KNNs) algorithm. This solution aims to address the sense of emergency due to the alarming statistics that highlight the mortality rate of cardiovascular patients. The project will enable a smart option based on IoT and ML to improve standards of living and prove crucial in saving human lives.

Elevated Levels of IL-33, IL-17 and IL-25 Indicate the Progression from Chronicity to Hepatocellular Carcinoma in Hepatitis C Virus Patients

Hepatitis C virus (HCV) is one of the most epidemic viral infections in the world. Three-quarters of individuals infected with HCV become chronic. As a consequence of persistent inflammation, a considerable percentage of chronic patients progress to liver fibrosis, cirrhosis, and finally hepatocellular carcinoma. Cytokines, which are particularly produced from T-helper cells, play a crucial role in immune protection against HCV and the progression of the disease as well. In this study, the role of interleukins IL-33, IL-17, and IL-25 in HCV patients and progression of disease from chronicity to hepatocellular carcinoma will be characterized in order to use them as biomarkers of disease progression. The serum levels of the tested interleukins were measured in patients suffering from chronic hepatitis C (CHC), hepatocellular carcinoma (HCC), and healthy controls (C), and their levels were correlated to the degree of liver fibrosis, liver fibrosis markers and viral load. In contrast to the IL-25 serum level, which increased in patients suffering from HCC only, the serum levels of both IL-33 and IL-17 increased significantly in those patients suffering from CHC and HCC. In addition, IL-33 serum level was found to increase by liver fibrosis progression and viral load, in contrast to both IL-17 and IL-25. Current results indicate a significant role of IL-33 in liver inflammation and fibrosis progress in CHC, whereas IL-17 and IL-25 may be used as biomarkers for the development of hepatocellular carcinoma.

COVID-19-related anxiety and knowledge toward its preventive measures among patients with chronic medical illness on follow-up in public hospitals of Bale, East Bale, and West Arsi zones, Ethiopia

Background: Coronavirus disease 2019, also known as 2019-nCoV cluster of acute respiratory illness with unknown causes, which occurred in Wuhan, Hubei Province, in China, was first reported to World Health Organization country office as of December 30, 2019. People with medical illness are at a higher risk for coronavirus disease, and the pandemic influences mental health and causes psychological problems, particularly in those with chronic medical illness. Hence, this study aimed to assess coronavirus disease 2019-related anxiety and the knowledge on its preventive measures among patients with medical illness on follow-up in public hospitals of Bale, East Bale, and Arsi zones Objective: To assess coronavirus disease 2019-related anxiety and knowledge toward coronavirus disease 2019 preventive measures among patients with chronic medical illness on follow-up in public hospitals of Bale, East Bale, and West Arsi zones. Methods: A hospital-based cross-sectional study was conducted in selected hospitals of Bale and West Arsi zones, Southeast Ethiopia. A total of 633 study participants were included in this study, and data were collected through an interviewer-administered questionnaire. A descriptive summary was computed. Bivariable and multivariable logistic regression analyses were carried out to identify the associated factors. Results: Overall, the prevalence of anxiety among chronic patients in this study was 6.3% (95% confidence interval: 4.6%–8.5%) and 420 (66.35%) had good knowledge on the preventive measures of coronavirus disease 2019. Factors significantly associated with anxiety among chronic patients were being educated (95% confidence interval: adjusted odds ratio = 0.26 (0.09–0.74)), being male (95% confidence interval: 2.69 (1.11–6.53)), and use of mask (95% confidence interval: 0.11 (0.05–0.26)). Conclusion: The prevalence of coronavirus disease 2019-related anxiety among chronic patients was high and being males, uneducated, and not using face mask was significantly associated with coronavirus disease 2019-related anxiety.

Changes in NK Cell Subsets and Receptor Expressions in HIV-1 Infected Chronic Patients and HIV Controllers

Natural killer (NK) cells are major effectors of the innate immune response and purported to play an influential role in the spontaneous control of HIV infection. In the present study, we compared the phenotypes of NK cells in the peripheral blood of three groups of subjects with chronic HIV-1 infection, HIV controllers, and healthy donors. The results showed that CD56+/CD16- NK cell subsets decreased in chronic patients and remained unchanged in controllers. Notably, we found that people living with chronic HIV-1 infection had suppressed NKp80, NKp46, and NKG2D expressions on NK cells compared to healthy donors, while HIV controllers remained unchanged. In contrast, NKG2D expression was substantially higher in controllers than in chronic patients (M=97.67, p&lt;0.001). There were no significant differences in inhibitory receptors KIR3DL1 and KIR2DL1 expressions. In addition, plasma cytokine IFN-γ, TNF-α and IL-12showed higher levels in HIV controllers compared to chronic patients. Overall, our study revealed that, as compared to chronic patients, HIV controllers show an increased activating receptors expression and higher number ofCD56+/CD16-NK cell subset, with increased expression levels of plasma cytokines, suggesting that higher immune activation in controllers may have a key role in killing and suppressing HIV.

Financial Burden and Health-seeking Behaviors Related to Chronic Diseases Under the National Health Insurance Scheme in Bolikhamxay Province, Lao PDR: A Cross-sectional Study

BackgroundFinancial protection is a key dimension of universal health coverage. In 2016, Lao PDR implemented a National Health Insurance system covering the entire population of certain provinces. This cross-sectional study investigated the health-seeking behavior and financial burden of households, including those with chronic patients, post coverage. MethodThe study was conducted in Bolikhamxay province from January 15 to February 13, 2019. In total, 487 households, selected via stratified random sampling, were surveyed, and questionnaire-based interviews were conducted. Health care service utilization and financial burden were examined.ResultsA total of 370 households had at least one member with some type of self-reported health problem within the last 3 months prior to the interview, while 170 had at least one member with a chronic condition. More than 75% of the households accessed a health facility when a member experienced health problems. The prevalence of catastrophic health expenditure (health expenditure/income between 20% and 40%) was 25.1% (threshold of 20%) and 16.2% (threshold of 40%). Through logistic regression, we found that the major factors determining financial catastrophes owing to health problems were household members with chronic illness, hospitalization, household poverty status, family size (both 20% and 40% thresholds), visiting a private facility (20% threshold), and distance from the province to the referral hospital (40% threshold).ConclusionsThe National Health Insurance system has positively impacted households’ access to health facilities. However, catastrophic health expenditure remains high, especially among chronic patients. Facilities under the National Health Insurance should be strengthened to provide more services, including care for chronic conditions.

Role of factors in eHealth literacy in period of COVID-19: a study of Turkey

PurposeThis study aims to understand the eHealth literacy skills of chronic patients and to explore the relations, patterns between eHealth literacy skills and different factors such as demographics, search strategies and health information sources and to explain their effects on eHealth literacy in Turkey in Izmir in COVID-19 outbreak.Design/methodology/approachA quantitative method was used in the study including a questionnaire. A total of 604 chronic patients responded to the questionnaire who applied the five popularly identified hospitals in Izmir in Turkey. CHAID analysis method was implemented to explore the strongest correlation between eHealth literacy and independent variables.FindingsUsing different social media types were correlated with patients’ eHealth literacy scores. Using Facebook, Twitter were the supportive predictors for the eHealth literacy scores. However, digital literacy was highly important for eHealth literacy.Originality/valueThis study shows that the social media channels which provides much more information such as Facebook and Twitter for the chronic patient. This could be beneficial for the eHealth tools and social media content developers in terms of the supply of health information. Moreover, the study gives ideas about the effect of digital literacy and the importance of health information provided.

Seeking Support, Posting Your Sorrows, and Getting Misinformed in Return A codebook for a quantitative content analysis on mental health online support groups (Preprint)

BACKGROUND The potential of the Internet to help chronic patients to cope with their condition was present from the times when it had become clear what types of services the new device would provide. Expected changes were beneficial, many thought, for communication between patients and physicians, patients and health care institutions. Some reserve was discernible when Web 2.0 came, and increased communication from patient to patient with it. OBJECTIVE Keeping this development in mind, the projected study is intended to find out how and why such online support groups for mental health can have negative outcomes for the people who turn there for help. It aspires to reach beyond the simple equations that communication between patients and physicians is good, while patient-to-patient communication is dangerous. METHODS A codebook for the content analysis of Facebook online support groups has been developed and a content analysis will be conducted on bundles of posts. Three consecutive periods of one year will be studied. The sample will consist of utterances in two groups, one moderated the other unmoderated. The major analysis will bring together indications of health care shortcomings, medical errors, and holding wrong health beliefs on the one side and conditions and perceptions on the other. Aside from a few analyses comparing different bundles, individual utterances will be the unit of analysis in most cases. A bundle will be selected (in a time-stratified sample) if a group member asks a declarative or procedural knowledge question, seeks help in making a decision, or wants to be emotionally supported. The design will allow three more minor perspectives: comparing moderated and unmoderated support groups, describing the effects of the COVID-19 pandemic by separating the study period into before Covid (year 2019), Year 1 (2020) and Year 2 (2021) of the COVID-19 Pandemic. RESULTS We demonstrated the usability of the proposed systematic framework with 11 threads (61 utterances) coded independently by two coders: using Krippendorff’s alpha, the coders met intercoder reliability (α = .88, range: .73- 1). CONCLUSIONS The codebook is a rigorous and standardized method for the analysis of discussions in online support groups. For discussion and interpretation, we expect unhealthy present relations between health literacy and patient empowerment or a development towards such a state to decisively explain an output by the health care system that falls short of an optimum.

AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications

Artificial Intelligence (AI) is the revolutionary paradigm to empower sixth generation (6G) edge computing based e-healthcare for everyone. Thus, this research aims to promote an AI-based cost-effective and efficient healthcare application. The cyber physical system (CPS) is a key player in the internet world where humans and their personal devices such as cell phones, laptops, wearables, etc., facilitate the healthcare environment. The data extracting, examining and monitoring strategies from sensors and actuators in the entire medical landscape are facilitated by cloud-enabled technologies for absorbing and accepting the entire emerging wave of revolution. The efficient and accurate examination of voluminous data from the sensor devices poses restrictions in terms of bandwidth, delay and energy. Due to the heterogeneous nature of the Internet of Medical Things (IoMT), the driven healthcare system must be smart, interoperable, convergent, and reliable to provide pervasive and cost-effective healthcare platforms. Unfortunately, because of higher power consumption and lesser packet delivery rate, achieving interoperable, convergent, and reliable transmission is challenging in connected healthcare. In such a scenario, this paper has fourfold major contributions. The first contribution is the development of a single chip wearable electrocardiogram (ECG) with the support of an analog front end (AFE) chip model (i.e., ADS1292R) for gathering the ECG data to examine the health status of elderly or chronic patients with the IoT-based cyber physical system (CPS). The second proposes a fuzzy-based sustainable, interoperable, and reliable algorithm (FSIRA), which is an intelligent and self-adaptive decision-making approach to prioritize emergency and critical patients in association with the selected parameters for improving healthcare quality at reasonable costs. The third is the proposal of a specific cloud-based architecture for mobile and connected healthcare. The fourth is the identification of the right balance between reliability, packet loss ratio, convergence, latency, interoperability, and throughput to support an adaptive IoMT driven connected healthcare. It is examined and observed that our proposed approaches outperform the conventional techniques by providing high reliability, high convergence, interoperability, and a better foundation to analyze and interpret the accuracy in systems from a medical health aspect. As for the IoMT, an enabled healthcare cloud is the key ingredient on which to focus, as it also faces the big hurdle of less bandwidth, more delay and energy drain. Thus, we propose the mathematical trade-offs between bandwidth, interoperability, reliability, delay, and energy dissipation for IoMT-oriented smart healthcare over a 6G platform.