Analysis Of Chronic Disease Research Articles

Analysis and Monitoring Application of Medical Data for Chronic Diseases in Elderly Living Alone Based on Wearable Medical Sensor Wristbands

This paper focuses on the analysis and monitoring application of medical data for chronic diseases in elderly individuals living alone using wearable medical sensor wristbands. It introduces the background, objectives, significance, content, and methods of the research. The development and application of wearable medical sensor wristbands and the status of chronic diseases in elderly living alone are discussed, highlighting existing research gaps. The section on the characteristics and needs analysis of chronic diseases in elderly living alone defines the term, analyzes common types and characteristics of chronic diseases, and examines the medical needs of such patients. The principles and technologies of wearable medical sensor wristbands are explained, covering working principles, common sensor technologies, and data collection and transmission technologies. The section on the collection and processing of medical data for chronic diseases in elderly living alone discusses data collection methods and steps, data processing and analysis methods, and data security and privacy protection. The design of a chronic disease medical monitoring system for elderly living alone based on wearable medical sensor wristbands is detailed, including system architecture and functional design, data visualization and report generation, and real-time monitoring and warning mechanisms. The experimental and results analysis section describes the experimental design and data collection methods, followed by an analysis and discussion of the results. In the summary and outlook section, the research work is summarized, existing problems and potential improvements are pointed out, and future developments are anticipated. This research provides new insights into the analysis and monitoring application of medical data for chronic diseases in elderly living alone using wearable medical sensor wristbands, offering a new approach to promoting health management and disease prevention for the elderly living alone.

A comprehensive analysis concerning eating behavior associated with chronic diseases among Romanian community nurses.

Lifestyle factors, including inadequate eating patterns, emerge as a critical determinant of chronic disease. Apart from caring for patients, nurses should also take an active role in monitoring and managing their own health. Understanding the intricate relationship between nurses' eating behavior and managing their own health is crucial for fostering a holistic approach to healthcare, therefore our study aimed to evaluate eating behavior and demographic factors influencing chronic disease prevalence in a sample of community nurses from Romania. Between October-November 2023, 1920 community nurses were invited to answer an online survey, using an advertisement in their professional network. Of them, 788 responded. In the survey, which included a semi-quantitative food frequency questionnaire with 53 food items, the Intuitive Eating Survey 2 (IES-2), and demographic items were used. A multivariate model was built for the prediction of the association between eating behavior and other factors associated with chronic diseases. The majority of participants were females (95.1%), with the largest age group falling between 40 and 49.9 years (48.2%). Regarding the EFSA criteria for adequate carbohydrate and fat intake, 20.2% of the group have a high intake of carbohydrates, respectively, 43.4% of the group have a high intake of fat. Analysis of chronic diseases indicated that 24.9% of individuals reported at least one diagnosis by a physician. The presence of chronic disease was associated with a low level of perceived health status, with an OR = 3.388, 95%CI (1.684-6.814), compared to those reporting excellent or very good perceived health status. High stress had an OR = 1.483, 95%CI (1.033-2.129). BMI had an OR = 1.069, 95%CI (1.032-1.108), while low carbohydrate diet score had an OR = 0.956, 95%CI (0.920-0.992). Gender and IES-2 did not significantly contribute to the model, but their effect was controlled. By unraveling the intricate interplay between nutrition, lifestyle, and health outcomes in this healthcare cohort, our findings contribute valuable insights for the development of targeted interventions and support programs tailored to enhance the well-being of community nurses and, by extension, the patients they support.

A spatially explicit analysis of chronic diseases in small areas: a case study of diabetes in Santiago, Chile

BackgroundThere is a strong spatial correlation between demographics and chronic diseases in urban areas. Thus, most of the public policies aimed at improving prevention plans and optimizing the allocation of resources in health networks should be designed specifically for the socioeconomic reality of the population. One way to tackle this challenge is by exploring within a small geographical area the spatial patterns that link the sociodemographic attributes that characterize a community, its risk of suffering chronic diseases, and the accessibility of health treatment. Due to the inherent complexity of cities, soft clustering methods are recommended to find fuzzy spatial patterns. Our main motivation is to provide health planners with valuable spatial information to support decision-making. For the case study, we chose to investigate diabetes in Santiago, Chile.MethodsTo deal with spatiality, we combine two statistical techniques: spatial microsimulation and a self-organizing map (SOM). Spatial microsimulation allows spatial disaggregation of health indicators data to a small area level. In turn, SOM, unlike classical clustering methods, incorporates a learning component through neural networks, which makes it more appropriate to model complex adaptive systems, such as cities. Thus, while spatial microsimulation generates the data for the analysis, the SOM method finds the relevant socio-economic clusters. We selected age, sex, income, prevalence of diabetes, distance to public health services, and type of health insurance as input variables. We used public surveys as input data.ResultsWe found four significant spatial clusters representing 75 percent of the whole population in Santiago. Two clusters correspond to people with low educational levels, low income, high accessibility to public health services, and a high prevalence of diabetes. However, one presents a significantly higher level of diabetes than the other. The second pair of clusters is made up of people with high educational levels, high income, and low prevalence of diabetes. What differentiates both clusters is accessibility to health centers. The average distance to the health centers of one group almost doubles that of the other.ConclusionsIn this study, we combined two statistical techniques: spatial microsimulation and selforganising maps to explore the relationship between diabetes and socio-demographics in Santiago, Chile. The results have allowed us to corroborate the importance of the spatial factor in the analysis of chronic diseases as a way of suggesting differentiated solutions to spatially explicit problems. SOM turned out to be a good choice to deal with fuzzy health and socioeconomic data. The method explored and uncovered valuable spatial patterns for health decision-making. In turn, spatial microsimulation.

Mining of Textual Health Information from Reddit: Analysis of Chronic Diseases With Extracted Entities and Their Relations

BackgroundSocial media platforms constitute a rich data source for natural language processing tasks such as named entity recognition, relation extraction, and sentiment analysis. In particular, social media platforms about health provide a different insight into patient’s experiences with diseases and treatment than those found in the scientific literature.ObjectiveThis paper aimed to report a study of entities related to chronic diseases and their relation in user-generated text posts. The major focus of our research is the study of biomedical entities found in health social media platforms and their relations and the way people suffering from chronic diseases express themselves.MethodsWe collected a corpus of 17,624 text posts from disease-specific subreddits of the social news and discussion website Reddit. For entity and relation extraction from this corpus, we employed the PKDE4J tool developed by Song et al (2015). PKDE4J is a text mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework.ResultsUsing PKDE4J, we extracted 2 types of entities and relations: biomedical entities and relations and subject-predicate-object entity relations. In total, 82,138 entities and 30,341 relation pairs were extracted from the Reddit dataset. The most highly mentioned entities were those related to oncological disease (2884 occurrences of cancer) and asthma (2180 occurrences). The relation pair anatomy-disease was the most frequent (5550 occurrences), the highest frequent entities in this pair being cancer and lymph. The manual validation of the extracted entities showed a very good performance of the system at the entity extraction task (3682/5151, 71.48% extracted entities were correctly labeled).ConclusionsThis study showed that people are eager to share their personal experience with chronic diseases on social media platforms despite possible privacy and security issues. The results reported in this paper are promising and demonstrate the need for more in-depth studies on the way patients with chronic diseases express themselves on social media platforms.

Analysis of Chronic Diseases in Technology Universities Faculty

As people’s life style, living environment, and nutritional and dietary pattern change, noninfectious chronic diseases have become common and frequently occurring diseases .In recent years, detection rate of chronic diseases in university teachers increases as time goes by and their health status is not optimistic. This paper summarizes documents illustrating the foregoing issue and indicates that hyperlipemia, hypertension and hyperglycemia etc. have become principal risk factors threatening the health of university teachers and staff members. Hyperlipemia, hypertension and hyperglycemia etc. are closely related with chronic diseases such as cardio-cerebrovascular disease and they interact as both cause and effect.Changing unhealthy life style and reducing blood lipid, blood pressure and blood glucose to normal level is critical to prevent cardio-cerebrovascular diseases, which is also important to prevent diseases among knowledgeable people。

Genética y epidemiología: posibilidad de un programa común desde la perspectiva de la historia de la biología

The current convergence of molecular genetics and epidemiology on the analysis of chronic diseases have prompted the comparison of the roles of genetics and epidemiology with those maintained at the end of XIX century by the, then, emergent bacteriology and miasmatic theories of infectious diseases. In some cases, the analogy have been extended to the causal role of germ and mutation on infectious and chronic diseases respectively. An historical analysis of the construction of the theory of the gene in the early years of our century, reveals that this conception was one of the difficulties that the new science of genetics had to overcome to elaborate the current theory of heredity which, therefore, seems incompatible with such concepts. It points also to the historical concepts that, although already obsolete, are still reflected in a deterministic view of the gene and, on the other hand, cast some optimism about the possibility of constructing a common program for genetic and epidemiology.

Method of Statistical Analysis of Chronic Disease in a Longitudinal Study of Illness

rT HE purpose of this paper is to present a detailed description of the method employed in the statistical treatment and presentation of data of chronic disease reported in the study of illness in the Eastern Health District of Baltimore (1). White families living in thirty-four city blocks formed the sample population. The plan of the study was to follow families that lived in a group of houses in certain blocks rather than to follow a selected group of families. No attempt was made to continue visiting families which moved out of these houses during the period of the study, but the new families that moved into the houses vacated in the sample blocks were included in the study. The record of illness started with the first visit to the family and each family was visited once a month thereafter.