Health-related Data Research Articles

Public Preferences for Digital Health Data Sharing: Discrete Choice Experiment Study in 12 European Countries.

With new technologies, health data can be collected in a variety of different clinical, research, and public health contexts, and then can be used for a range of new purposes. Establishing the public's views about digital health data sharing is essential for policy makers to develop effective harmonization initiatives for digital health data governance at the European level. This study investigated public preferences for digital health data sharing. A discrete choice experiment survey was administered to a sample of European residents in 12 European countries (Austria, Denmark, France, Germany, Iceland, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) from August 2020 to August 2021. Respondents answered whether hypothetical situations of data sharing were acceptable for them. Each hypothetical scenario was defined by 5 attributes ("data collector," "data user," "reason for data use," "information on data sharing and consent," and "availability of review process"), which had 3 to 4 attribute levels each. A latent class model was run across the whole data set and separately for different European regions (Northern, Central, and Southern Europe). Attribute relative importance was calculated for each latent class's pooled and regional data sets. A total of 5015 completed surveys were analyzed. In general, the most important attribute for respondents was the availability of information and consent during health data sharing. In the latent class model, 4 classes of preference patterns were identified. While respondents in 2 classes strongly expressed their preferences for data sharing with opposing positions, respondents in the other 2 classes preferred not to share their data, but attribute levels of the situation could have had an impact on their preferences. Respondents generally found the following to be the most acceptable: a national authority or academic research project as the data user; being informed and asked to consent; and a review process for data transfer and use, or transfer only. On the other hand, collection of their data by a technological company and data use for commercial communication were the least acceptable. There was preference heterogeneity across Europe and within European regions. This study showed the importance of transparency in data use and oversight of health-related data sharing for European respondents. Regional and intraregional preference heterogeneity for "data collector," "data user," "reason," "type of consent," and "review" calls for governance solutions that would grant data subjects the ability to control their digital health data being shared within different contexts. These results suggest that the use of data without consent will demand weighty and exceptional reasons. An interactive and dynamic informed consent model combined with oversight mechanisms may be a solution for policy initiatives aiming to harmonize health data use across Europe.

Governance of research and product improvement studies in consumer mental health apps. Interviews with researchers and app developers

ABSTRACT Consumer mental health apps (MHAs) collect and generate mental health-related data on their users, which can be leveraged for research and product improvement studies. Such studies are associated with ethical issues that may be difficult for researchers and app developers to assess. To improve ethical study conduct, governance through rules, agreements and customs could be relied upon, but their translation into practice is subject to barriers. This qualitative interview study with 17 researchers and app developers looked into the role and impact of governance standards on consumer MHA studies. Interviewees experienced a significant number of rules, agreements and customs, although not all of the governance standards that can potentially be applicable. Standards did have an impact on the interests of researchers and app developers, app users and society, but this impact was mediated by several barriers related to their conceptualization and implementation. Conceptualization barriers impacted the development of a standard, the inclusion of relevant concepts and the coordination between standards. Implementation barriers concerned the resource cost of understanding a standard, as well as suboptimal enforcement. The framework developed in this study can support more effective efforts to improve the governance of future consumer MHA studies.

Participants’ Perceptions of Privacy and Data Sharing Regarding Health-Related Data Using Artificial Intelligence

Substantial research has been performed to explore the effective integration of artificial intelligence (AI) into healthcare. Consequently, it becomes imperative to grasp the perspectives and concerns of general public in relation to the utilization of their health data in AI research, particularly within the context of privacy issues. This study aims to explore the awareness of participants about the AI, their privacy concerns about data sharing using AI and its use on healthcare data. We carried out a comprehensive study by employing a self-administered questionnaire tool with participants using convenience sampling methodology. A total of 450 participants were enlisted from Saudi Arabia. Conditional binary logistic regression models were employed to compute odds ratios (ORs) and 95% confidence intervals. Among the participants, 168 (37.3%) showed that they have knowledge about AI. In terms of personal data’s vulnerability when using AI technology, 186(41.3%) perceived a privacy risk about their health data. 201(44.7%) indicated their trust in AI’s ability to safeguard data privacy. Regarding the use of machine learning for medical record analysis, 180(40.0%) declared it riskier than benefits. For AI research purposes, 205 (45.6%) supported data sharing and 213(47.3%) believed hospitals should have strict regulations while 214 (47.6%) believed hospitals should provide limited access to data to ensure health data privacy. Furthermore, the study found that younger individuals were more likely to trust AI with their data privacy (OR = 0.540, 95% CI; 0.300-0.972), while participants with higher education levels were nearly three times more likely to trust AI with their data privacy compared to those with lower education (OR = 2.894, p = 0.047, 95% CI; 1.012-8.278). Patients’ viewpoints, the extent of assistance they receive, and their comprehension of health data research and artificial intelligence exhibited significant variations, often influenced by privacy issues. To ensure the acceptability of AI research and its seamless integration into clinical practice in the future, it is imperative to engage the public more extensively and stimulate discussions, particularly concerning privacy concerns.

Neuroadaptive Incentivization in Healthcare using Blockchain and IoT

Financially incentivizing health-related behaviors can improve health record outcomes and reduce healthcare costs. Blockchain and IoT technologies can be used to develop safe and transparent incentive schemes in healthcare. IoT devices, such as body sensor networks and wearable sensors, etc. connect the physical and digital world making it easier to collect useful health-related data for further analysis. There are, however, many security and privacy issues with the use of IoT. Some of these IoT security issues can be alleviated using Blockchain technology. Incorporating neuroadaptive technology can result in more personalized and effective therapies using machine learning algorithms and real-time feedback. The research investigates the possibilities of neuroadaptive incentivization in healthcare using Blockchain and IoT on patient health records. The core idea is to incentivize patients to keep their health parameters within standard range thereby reducing the load on healthcare system. In summary, we have presented a proof of concept for neuroadaptive incentivization in healthcare using Blockchain and IoT and discuss various applications and implementation challenges.

Childhood Health and Growth Trends: A Cross-Sectional Study of School Children

Childhood is a pivotal phase of human development, entails rapid growth and evolving health requirements. This cross-sectional study examines the age and sex distribution of school children across five coastal localities. To address the unique health and well-being challenges faced by school children in different regions, this cross-sectional study explores the age and sex distribution of school children across five distinct localities: Koilpathu, Karaikalmedu, Nallathur, Varichikudy, and Kottucherry. It offers a comprehensive examination of school children, encompassing demographic profiles, anthropometric measurements, and a wide array of health-related data. Our analysis revealed shifting gender proportions across age groups, with females dominating in the younger years but males surpassing females as children grow older. Particularly intriguing were the 12-year age group's scarcity of females (0.3%) and the 13-year age group's exclusive female composition (6.07%). Regarding body mass index (BMI), boys generally exhibited values near reference standards in their early years, while girls displayed lower BMI values than the reference during pre-adolescence, signaling potential nutritional concerns. Malnutrition was less prevalent in younger age groups, but overweight and obesity became more common as children aged. Gender-specific health disparities were also evident, with females more susceptible to anemia, while males had a higher prevalence of color blindness and dental health issues. These findings underscore the importance of tailored interventions to address age and gender-specific health challenges among coastal school children.

E-Health technological barriers faced by Iraqi healthcare institutions

The health records management issues have detrimentally affected the Iraqi healthcare sector resultant from the inferior information technology integrity and the complicatedness of data. In order to resolve this problem, other methods of storage, management, and retrieval of health-related data can be offered by e-Health services. These aspects are important in tracking patients’ health conditions using multiple platforms at the service provider’s own convenience. However, there are numerous issues that hinder the extensive adoption of e-Health services by the health establishments in Iraq, such as issues on security and privacy, legalities connected to policies, and its implementation. The significance of the current study is its identification of the crucial aspects that will lead to the success of impacting the technical staff towards their positive acceptance and behavior with regard to the employment of e-Health information system in Iraqi hospitals. A self-administered survey was carried out on 104 technical staff from various healthcare organizations in Iraq using a simple random sampling technique. A nonparametric second-generation multivariate analysis was conducted on the compiled ordinal data by the utilization of the PLS-SEM approach. The outcomes indicated the favorable impact of several factors on the doctor’s employment of e-Health in Iraqi hospitals, comprising Availability and Affordability of the hardware and software, ICT Support Service, Network Reliability, Privacy, and Security. The results are important in assisting the comprehension of e-Health systems in the management of health data, in addition to the provision of the pertinent recommendations for policymakers to provide guidance, issue advice, directives to the healthcare professionals toward the continuous consideration of using advance information and communications technology at work.

Symposium 04: Innovative Ways of Applying Digital Technology in Neuropsychology - A Sneak Peak into the Future

Summary Abstract:The explosion of digital technology in the past decade has led to unprecedented possibilities towards improving cognitive assessment and understanding brain health. Digital technology encompasses using a multitude of devices, such as laptops, smartphones, etc, to collect health-related data. The settings can be varied to include in-clinic, remote/virtual, or a combination of hybrid models for data collection. Data can be collected at a single time point or over a continued period of time. Furthermore, the unique combination of devices used, settings, and methods of collecting digital data can become even more exclusive against the backdrop of the 'purpose’ for conducting the digital study. This symposium, consisting of four abstracts, brings together the unique combination of digital studies with exclusive devices, methodologies, settings, and purposes. The topics range from how smartphone-based assessments can be applied to understand the interaction between day-to-day variability in sleep and cognition, to the use of computerized testing to investigate the associations between cognitive performance and markers of brain pathology (e.g. amyloid and tau status), to understanding cognition from an open-source smartphone application to passively and continuously capture sensor data including global positioning system trajectories, to the development and validation of an online simulated money management credit card task, and to determining the effects of cognitive rehabilitation via digital technology on cognition, neuropsychiatric symptoms, and memory strategies.

Prototipe Rekam Kesehatan Personal pada Pasien Diabetes Mellitus (Literature Review)

Personal Health Record (PHR) is a collection of health-related data that is archived and managed by an individual or parties associated with the PHR. Its purpose is to provide a comprehensive and accurate summary of a person's health history that can be accessed online. This research is a Literature Review study which aims to determine the features and implementation of Personal Health Records in Patients with Diabetes Mellitus. The method used is the Literature Review method from 7 journals which uses several methods including the prototype method, qualitative methods, heuristic evaluation methods, applied development methods, participatory action research methods, and randomized controlled trial methods with data collection techniques through google scholar, direct science and pubmed. The results of the research are seen in the features and implementation of the Personal Health Record. The Personal Health Record feature for diabetes mellitus with dialysis measures has 25 features on the main page, the PHR feature for diabetes mellitus has 7 main features. PHR implementation allows users to access their personal health records anytime and anywhere. Thus, the PHR system can increase accessibility and allow users to access PHR without time restrictions and also the Personal Health Record can assist in monitoring the health condition of Diabetes Mellitus patients. Research conclusion According to the journals analyzed, PHR is effective because it can encourage patient participation in individual health controls, and can access information about diabetes mellitus through monitoring physical activity, diet, weight, and glucose levels.

Effects of a telehealth program for wound healing promotion on the wound healing level in patients with diabetes undergoing coronary artery bypass graft surgery: A randomized controlled trial.

Patients with diabetes undergoing coronary artery bypass graft (CABG) are at risk of delayed wound healing. The telehealth program for wound healing promotion (TPWHP) was developed to improve wound healing in patients with diabetes undergoing CABG in a university hospital. This study aimed to examine the effects of the TPWHP on the healing of the sternal wound and saphenous vein graft (SVG) donor site in patients with diabetes undergoing CABG. In this randomized controlled trial, the participants were randomly assigned to the intervention (n = 30) and control (n = 30) groups. The control group received routine nursing care, whereas the intervention group received routine nursing care along with the TPWHP, which provides education and wound monitoring using multimedia and a surgical wound care booklet and monitors the wound through the LINE application on a smartphone after hospital discharge. Data were collected from June to November 2020 at two university hospitals in Bangkok, Thailand, using the demographic and health-related data form and the Thai wound assessment inventory (WAI). Data were analyzed using the Chi-squared, Fisher's exact, and Mann-Whitney U tests. The intervention group had significantly lower mean scores-indicating favorable wound healing-for SVG donor site wound healing level than the control group on days 14-21 after surgery (p = 0.012, mean = 0.58 ± 0.54 and 1.08 ± 0.82, respectively). No significant intergroup difference was observed in terms of the sternal wound healing level (p = 0.126); however, the intervention group showed lower mean scores-indicating good wound healing-than the control group. The TPWHP promotes effective wound healing of the SVG donor site; however, its efficacy on sternal wound healing was uncertain. Therefore, nurses should implement the TPWHP in collaboration with a multidisciplinary team to improve wound healing after hospital discharge. Thai Clinical Trials Registry (TCTR20211112002).

1.G. Round table: European Health Data Space - Development of a European metadata catalogue

Abstract How to find who collects health-related data, where is this data stored and how to access it for research purposes on public health issues, policy making and crisis response? The recent COVID-19 pandemic, amongst others, brought to light two key aspects about health data in Europe; the complex and very heterogeneous landscape of health-related data collections and the lack of adherence of these collections to the FAIR principles, compromising their re-use by researchers and policy makers. More specifically, studies have shown that one of the main barriers to the re-use of health data has been the lack of findability, the lack of an inventory listing all kinds of datasets that can be made available and the way these datasets can be accessed. Such an inventory can be a metadata catalogue. A catalogue composed of metadata records, one for each dataset describing information about the dataset without giving access to the actual data. Considering the above findings, the need to have a common metadata catalogue at European level, presenting the health-related data that can be made available for secondary use, the European Commission drafted a legislative proposal for the creation of a European Health Data Space (EHDS). In this proposal the development of a European metadata catalogue has been mentioned in articles 38, 55 and 57. To start designing, developing and implementing some of the business capabilities mentioned in the legislative proposal, the Commission funded a 2-year project, the HealthData@EU pilot project. As part of this project, Sciensano is leading the work on the development of a standardised metadata record template for health-related datasets based on an already existing standard, the DCAT-AP. The aim is to extend this standard with a metadata vocabulary fit for the purpose of health-related datasets and hence designing and implementing the Health DCAT-AP standard. In this workshop we invite participants to learn about the developments of the HealthData@EU pilot project and, more specifically, the design of the European metadata record template for health-related datasets. Participants will have the opportunity to discover the latest developments regarding the HealthData@EU pilot project and the current situation in Europe regarding findability of health data, metadata catalogues and metadata standards. Moreover, they will get acquainted with the DCAT-AP metadata standard, the reasoning behind its use and practical examples from two other catalogues that are currently using it to describe health-related data collections, namely the European Health Information Portal and the EJPRD virtual platform. Finally, we will present the newest extension of this standard and will interact with the audience, using mentimeter, to get an idea of their understanding and their opinion regarding the additional health-related properties. Key messages • To learn the latest developments of the HealthData@EU pilot project, especially regarding the metadata catalogues. • In depth description of a health extension to the DCAT-AP metadata record standard template with two real-life examples. Speakers/Panelists Camille Cloitre Health Data Hub, Paris, France Miriam Saso Sciensano, Brussels, Belgium Pascal Derycke Sciensano, Brussels, Belgium Marc Hanauer EJPRD, Paris, France Truls Korsgaard Norwegian Directorate of e-health, Norway

Adherence to the Mediterranean Diet and Urinary Sodium Excretion in people at high risk of dementia

Abstract Background Both adequate sodium intake and adherence to the Mediterranean Diet (MedDiet) are largely described as important factors for the prevention and control of hypertension and cerebrovascular diseases. However, data regarding sodium intake in the MedDiet is scarce. This study aims to quantify the association between the MedDiet adherence and sodium excretion. Methods This cross-sectional analysis was carried out under the MIND-Matosinhos randomized controlled trial, a community-based program for Portuguese adults at high risk of dementia. The adherence to the MedDiet was assessed through the Portuguese version of the Mediterranean Diet Adherence Screener questionnaire, and a score ≥10 indicated a high adherence. A 24h urine collection was used to evaluate creatinine and sodium excretion. Excessive sodium excretion was defined as ≥ 2000 mg/day. Sociodemographic, lifestyle and health-related data were obtained through structured questionnaires. The association between the adherence to the MedDiet and urinary sodium excretion (recoded into two categories by the median) was quantified by calculating Odds Ratios (OR) and respective 95% Confidence Intervals (95% CI) using a binary logistic regression. Results A total of 126 individuals (24 to 83 years old; median age of 70.0) was included in this study (58.7% female). High adherence to the MedDiet was observed among 15.9% of the sample whereas excessive sodium excretion was reported by 84.1%. After adjusting for confounders, participants with high adherence to the MedDiet showed a higher sodium excretion (OR = 4.05; 95% CI: 1.33-12.37). Conclusions A high adherence to the MedDiet was associated with a higher sodium excretion. Thus, an increased adherence to this dietary pattern seems to have limited effects on the adequacy of sodium intake. This highlights the importance of public health measures to reduce sodium intake when following a MedDiet namely by improving consumer empowerment. Key messages • Despite the well-known protective effect of the Mediterranean Diet on hypertension and cerebrovascular diseases, the role of sodium intake within this dietary pattern is still understudied. • The Mediterranean Diet can be an important source of hidden sodium, which enhances the need to invest in public health measures to reduce sodium intake, namely through culinary medicine education.

Feasibility of Health and Fitness Data Collection in Online Behavioral Intervention of Elderly

Abstract The global increase in life expectancy has led to a growing elderly population, which necessitates novel strategies to promote healthy ageing and maintain optimal quality of life. Lifestyle coaching has emerged as a promising intervention to address these challenges by offering tailored guidance and support for seniors to make sustainable lifestyle changes. This study investigates the feasibility of collecting health and fitness data reported by the elderly population participating in an online behavioural intervention. We recruited 60 community-dwelling elderly individuals between the ages of 55-80 (including both men and women) for a 14-week online intervention promoting healthy lifestyles using coaching principles. The main criteria for inclusion were a diagnosis of arterial hypertension with or without medication and/or overweight or obesity as defined by BMI. We collected health-related data (arterial blood pressure, weight, waist circumference, quality of life using EQ-5D-5L questionnaire) and fitness-related data (30-second chair test, 2-minute step test). All data were self-recorded by the participants and reported via an online system with the help of a health coach. Forty-seven participants were able to complete the entire 14-week intervention. The initial data set resulted in only 51% (24 out of 47) of the participants having reported the full dataset. We then used written and spoken reminders to notify participants to submit the collected data, which brought the percentage up to 70% (33 out of 47). The main obstacle in health and fitness-related data collection was the forgetfulness of participants to do the self-reported measurements and tests. Among other factors affecting the amount of collected data, we experienced a lack of understanding of the process of the fitness tests, as well as a low questionnaire completion ratio. Almost 80% of participants managed to report all of the health-related data which was also considered the least complicated to collect. Key messages • Elderly people with higher cardiovascular risk are able to self-report health and fitness data in online behavioral intervention but thorough guidance and reminders need to be applied. • Elderly people are willing to participate on the online lifestyle behavioural interventions.

Brain tumor image identification and classification on the internet of medical things using deep learning

The health services research network is showing a lot of interest in the Internet of Medical Things (IoMT). In IoMT, the Internet is used to help compile important health-related data. A brain tumor is caused by a mass of random cells inside the brain, which is dangerous and harmful to the brain. Today, it is difficult to accurately recognise brain images. In order to find and correctly categorize malignant cells in recognizing brain pictures, this research offers a support value-based deep neural network (SDNN) in e-Health care administration utilizing the IoMT innovation. As a starting point, a database of investigation is created using picture data based on IoT innovation and clinical images. The input brain picture is subjected to skull stripping during the preprocessing stage in order to isolate the desired brain area. The preprocessed output pictures are then used to extract the useful characteristics, such as entropy, geometric, and texture features. Finally, based on the collected characteristics, the proposed support value based adaptive deep neural network (SDNN) identification classifies the brain pictures as normal or abnormal. The results of the experiments are examined to show how the suggested recognition approach outperforms the ones already in use.

DATA JOURNALISM IN HEALTH REPORTING IN BANGLADESH: PATTERN OF PRACTICES, IMPEDIMENTS AND PREPARATIONS FOR JOURNALISTS

Data journalism, with its ability to provide macro and micro-scale perspectives on health care, redefined health reporting during the global COVID-19 pandemic. Under these circumstances, health has taken the place of high significance. Consequently, the curiosity to see how data journalism is exercised while covering health issues in Bangladesh led to the idea of this study. This investigation aimed to shed light on how data journalism was applied while reporting on health in Bangladeshi media. The study was conducted following a mixed-method approach; quantitative data through content analysis and qualitative data through key informant interviews. The news published in August and September of 2022 in online editions of The Business Standard, the Prothom Alo, the Daily Star and the Ittefaq; was taken for content analysis. Additionally, four key informant interviews with academicians, journalism trainers and media development experts were conducted. The study concluded that data journalism practices in Bangladesh are not up to the standard. The scenario is also severely poor in terms of the number of published data stories; only 24 reports among 736 health-related news were data-driven stories. The Business Standard published 19 health-related data stories, which is the highest among the four news outlets. On the other hand, the Prothom Alo and the Ittefaq did not publish any health-related data story. Even the data-driven stories published in these four news outlets focused only on urban areas. Additionally, the data-driven stories were mainly reported based on government sources. Besides, among 25 data visuals, 84% were pictographs. However, the interviewees identified not having an Optical Character Recognition (OCR) tool for the Bengali language as a significant impediment to the flourishment of data journalism in Bangladesh. They also marked that, journalists in our newsrooms often do not possess the technical skills to carry out the data-driven story. Nevertheless, they proposed that, to improve the scenario of data journalism in Bangladesh, news industry and the universities should work together. Media development organizations should advocate the concept of data journalism in the newsrooms. Furthermore, they have to introduce workshops and training programs on data journalism.

Designing for Improved Patient Experiences in Home Dialysis: Usability and User Experience Findings From User-Based Evaluation Study With Patients With Chronic Conditions.

Chronic kidney disease affects 10% of the population worldwide, and the number of patients receiving treatment for end-stage kidney disease is forecasted to increase. Therefore, there is a pressing need for innovative digital solutions that increase the efficiency of care and improve patients' quality of life. The aim of the eHealth in Home Dialysis project is to create a novel eHealth solution, called eC4Me, to facilitate predialysis and home dialysis care for patients with chronic kidney disease. Our study aimed to evaluate the usability, user experience (UX), and patient experience (PX) of the first version of the eC4Me solution. We used a user-based evaluation approach involving usability testing, questionnaire, and interview methods. The test sessions were conducted remotely with 10 patients with chronic kidney disease, 5 of whom had used the solution in their home environment before the tests, while the rest were using it for the first time. Thematic analysis was used to analyze user test and questionnaire data, and descriptive statistics were calculated for the UMUX (Usability Metric for User Experience) scores. Most usability problems were related to navigation, the use of terminology, and the presentation of health-related data. Despite usability challenges, UMUX ratings of the solution were positive overall. The results showed noteworthy variation in the expected benefits and perceived effort of using the solution. From a PX perspective, it is important that the solution supports patients' own health-related goals and fits with the needs of their everyday lives with the disease. A user-based evaluation is a useful and necessary part of the eHealth solution development process. Our study findings can be used to improve the usability and UX of the evaluated eC4Me solution. Patients should be actively involved in the solution development process when specifying what information is relevant for them. Traditional usability tests complemented with questionnaire and interview methods can serve as a meaningful methodological approach for gaining insight not only into usability but also into UX- and PX-related aspects of digital health solutions.

Personality traits and health-related behaviors in medical students facing a stressful event.

It is believed that personality traits have an impact on the propensity to change and maintain favorable lifestyle habits. This issue has been raised by multiple studies, however, none of them appeared to focus on population under severe psychological stress. The aim of the present study was to investigate the link between personality traits and health-related behaviors and measures such as dietary intake of specific food products, physical activity, body-mass index and the use of cigarettes in medical students facing a stressful event. The study included a cohort of third-year medical students from the Medical University of Lodz, Poland, facing a stressful subject exam during the first COVID-19-related lockdown. At baseline, personality traits were evaluated with the use of the Polish version of the Big Five Inventory-Short questionnaire. Then, consumption of selected food products was monitored with the use of seven-day electronic dietary record. Also, some other health-related data was collected (body-mass index, physical activity and the use of cigarettes). General Linear Modeling techniques, logistic regression and exploratory factor analysis were applied to analyze the data. Four hundred and forty-four students completed the study. A two-factor pattern of food consumption was discovered by the exploratory factor analysis in the study group (34% of the variance explained). Higher conscientiousness, but not the other personality traits, was found to be significantly associated with generally healthier lifestyle manifested by higher consumption of vegetables, wholegrain products, fruits and nuts (adjusted beta 0.16, 95%CI 0.06 to 0.26, pη2 = 2.3%, p = 0.0015) and lower cigarette smoking (adjusted odds ratio 0.84, 95%CI 0.75 to 0.94, p = 0.0020), but insignificantly with physical activity and body-mass index. Severely stressed medical students expressing high conscientiousness tend to present healthier behaviors. Therefore, interventions aimed at improving lifestyle habits in students with low conscientiousness might be useful.

Intelligent privacy preserving deep learning model for securing IoT healthcare system in cloud storage

Nowadays the existing legacy management-based healthcare system maintains and processes a large amount of health-related data. The widespread adoption of the Internet of Things (IoT) and its progressive development have promised the way for the development of IoT-enabled healthcare with impressive data processing and big data storage capabilities. Intelligent medical healthcare intends to offer a framework to remotely monitor users’ health-related data as the Industrial Internet of Things (IIoT) develops. Because they are stored on a cloud server, the data are still susceptible to manipulation and privacy breaches. The Keras Xception Deep Learning System (KX-DLS) with Dynamic Searchable Symmetric Encryption (DSSE) scheme is a revolutionary IoT-based deep learning intelligent privacy-preserving system that is advantageous for digital healthcare and its functionalities to handle security-related challenges. The dataset is being used to pre-train the system, and users’ personal information is kept separate in a secure location. Without knowing any personal information about the users, we analyse health-related data stored in the cloud and build a sophisticated security framework based on a deep learning model. With the most extensive collection of security features, our framework for learning intelligent privacy preservation optimizes the system to guarantee high data integrity and few privacy breaches. As a result, it may be useful in situations where users employ mobile devices with limited resources to engage a healthcare cloud system for extensive virtual health services, and the results of this research show that it has been a better-secured model in comparison with state-of-the-art previous techniques.

The Influence of Exposure to Pesticides on Plasma Cholinesterase Levels and Hepatic, and Renal Functions among Farmers in Khan Yunis Governorate, Palestine

Background: An increase in acute intoxication signs/symptoms indicates a serious public health problem among farmers who use organophosphate (OP) pesticides. This study aimed to assess the health effects of OP pesticides on farm workers who deal with pesticides in Khan Yunis Governorate, Palestine. Methods: A comparative cross-sectional study started in March and finished in September 2021 on a total of 120 randomly selected farmers, out of them 60 subjects that used pesticides (cases group) for six months or more. The other 60 were not exposed to pesticides (controls group), aged between 20-50 years old. Sociodemographic data, health-related data, and working-related data habits of the study population were taken by interview questionnaire. Biochemical evaluations were carried out. Collected data were analyzed using SPSS 22. Results: The prevalence of abnormal plasma cholinesterase (PChE) activity among the cases group due to chemical poisoning was significantly higher as compared to those in the control group (p ≤ 0.05). The case group has higher levels of serum liver enzymes (AST, ALT, & ALP) when compared with the control group (p ≤ 0.05 for all). The kidney function indicators (creatinine and urea) were significantly higher among the cases group as compared to the control group (p ≤ 0.05 for all). The occurrence of headaches is associated with abnormal PCHE activity among farmers who on pesticides. Conclusion: The decrease in PCHE activity and increase in serum AST, ALT, ALP, urea & creatinine that were observed among farmers who were on pesticides-using in the current study indicate the adverse and toxic effects of pesticides on the liver and kidney functions among them, so these observations should be of interest to health and environmental policymakers in Palestine.

Цифровая трансформация в первичном звене здравоохранения и управление на основе данных.

In the contemporary healthcare context, there exists a necessity to optimize the organization and management of patient diagnosis, routing, and treatment, especially within the primary care sector. Within this scope, digital transformation assumes a pivotal role, offering novel avenues for enhancing the quality of medical assistance. Research Objective: This study aims to identify the fundamental issues pertaining to the organization and management of patient diagnosis, routing, and treatment within the primary care sector. Furthermore, the study seeks to formulate recommendations for addressing these challenges through the avenue of digital transformation. Materials and methods. The study was conducted through the utilization of comparative analysis employing synthesis, analysis, modeling, and specialization methodologies. Data were acquired through a specially developed questionnaire encompassing inquiries regarding healthcare process organization and management. Discussion and results. The outcomes of the investigation elucidate notable disparities between the organization and management of healthcare processes within the primary care sector and the inpatient setting. Notably, the application of digital technologies is underscored for its capacity to enhance patient-physician communication, reduce transactional overheads, and facilitate the effective collection and analysis of health-related data. Conclusion. The significance of digital transformation is underscored in enhancing patient satisfaction with medical care, optimizing processes, and elevating the quality of medical assistance within the primary care domain.

Health Problems Related to Presenteeism Among Japanese Employees.

This study identified primary health problems related to presenteeism stratified by sex. A cross-sectional study was conducted to analyze the relationship between 26 health problems and presenteeism using data from health-related data and World Health Organization Health and Work Performance Questionnaire among 12,526 employees in Japan. Sex-stratified multiple regression analyses showed that presenteeism was associated with nine health problems in both sexes, depressive symptoms, followed by lack of appetite, insufficient sleep, heart palpitations, or shortness of breath demonstrated the strongest relationship. Men had five additional health problems associated with presenteeism; furthermore, the relationships of those were stronger than women. The primary health problems related to presenteeism were depressive symptoms and indefinite complaints in both sexes. Men had more and stronger relationship between health problems and presenteeism than women.