Health Profile Of Individuals Research Articles

Impact of Comorbidities and Skin Diseases on Post-Vaccination Reactions: A Study on COVID-19 Vaccinations in Poland.

Background: The COVID-19 pandemic necessitated rapid and widespread vaccination efforts, which proved critical in reducing the severity and mortality of the virus. However, the interplay between vaccinations, pre-existing skin conditions, and other comorbidities still needs to be explored. This study investigated the occurrence and severity of adverse events following immunization (AEFIs) with COVID-19 vaccines in individuals with chronic skin diseases and comorbidities within a Central European cohort. Methods: An anonymous online survey was conducted between May 2022 and February 2023, targeting students and employees of universities in Wrocław, Poland. A total of 513 respondents were analyzed, focusing on AEFIs following the first, second, and third doses of COVID-19 vaccines and the effects of COVID-19 on conditions such as atopic dermatitis, psoriasis, vitiligo, acne vulgaris, rosacea, and various comorbidities. Results: COVID-19 vaccination effectively protected against severe disease across all doses. The analysis revealed no significant impact of either COVID-19 infection or vaccination on the course of selected skin diseases and comorbidities. The reporting of AEFIs to the Sanitary Inspection was notably low. The Moderna and Pfizer mRNA-based vaccines were associated with a higher reported number of AEFIs, particularly after the second and third doses, compared to AstraZeneca, which exhibited fewer adverse events after subsequent doses. Conclusions: COVID-19 vaccination is both safe and effective, even in patients with pre-existing skin conditions and comorbidities. Vaccine selection may benefit from considering individual health profiles, and better reporting of AEFIs is needed to enhance vaccine safety monitoring.

Molecular and cellular level analysis of the mechanism of nutritional intervention in preventing epidemic virus infection

Molecular nutrition encompasses a wider range of investigations than nutritional genomics, which can be considered a scientific investigation of how different nutrients and dietary components influence the cellular and molecular mechanisms of the body and health. In effect, increasing antioxidant elements in daily diets can assist with combating the inflammatory response caused by cytokines in the human body. Antioxidants can impede the oxidation process and hence avoid the creation of free radicals in the cytoplasm that can damage the cells via chain reactions. Contamination sequences, batch effects, uneven sampling, unreported taxa, technological biases, and heteroscedasticity are all significant issues in molecular microbial biology. Artificial Intelligence (AI) methodologies to uncover hidden patterns, connections, and interactions within metabolomics data, allowing them to provide personalized food recommendations based on individual health profiles. A multi-scale convolutional neural network (MCNN) classifies cellular images into phenotypes in a single coherent step utilizing the image’s pixel intensity values. Hence, the proposed AI-MCNN has been an essential nutrient that assists the immune system in various ways, including acting as an antioxidant to protect healthy cells, promoting immune cell development and function, and creating antibodies. According to epidemiological research, people who are undernourished are more likely to get bacterial, viral, and other diseases. Nutritional epidemiology investigates dietary or nutritional parameters concerning illness prevalence in communities. Nutritional epidemiology results often add to the evidence that provides dietary recommendations for preventing disease and related disorders.

Enhancing personalized medicine through molecular diagnostics: Implications for nursing care

The advent of molecular diagnostics, exemplified by technologies such as Polymerase Chain Reaction (PCR) and gene sequencing, represents a pivotal shift towards personalized medicine, fundamentally altering the landscape of healthcare delivery and nursing care. This paper delves into the transformative potential of genetic screening and risk assessment, disease typing and classification, and predictive analysis of treatment responses, highlighting the critical role of molecular diagnostics in facilitating a more nuanced understanding of individual health profiles and tailoring treatment accordingly. The integration of genetic data into treatment planning enables healthcare practitioners to optimize therapeutic strategies, significantly improving patient outcomes. However, this advancement is not without challenges. The ethical considerations surrounding data privacy, the complexity of genetic information, and issues of cost and accessibility are scrutinized, underscoring the need for robust ethical guidelines, continuous professional education, and policy interventions to ensure equitable access to these technologies. Through a comprehensive review, this paper illustrates the intricate interplay between molecular diagnostics and personalized nursing care, advocating for an interdisciplinary approach to harness the full potential of these technological advancements in improving patient care.

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins.

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

Nadifit, data-driven diagnosis: a clinical study for evaluating the significance of traditional Chinese medicine organ patterns through pulse diagnosis (nadi pariksha) for accurate pathology predictions

Background: Pulse diagnosis or nadipariksha, is a traditional diagnostic technique utilized in ayurveda and traditional Chinese medicine, analyzing various pulse characteristics such as force, patterns, rate, and rhythm to discern underlying health conditions. This method integrates TCM's five-element energy levels and Ayurveda's Tridosha levels to accurately identify the root cause of diseases, enabling practitioners to tailor treatments accordingly. In today's fast-paced world, the need for efficient health monitoring is imperative. However, finding expert practitioners proficient in pulse diagnosis is becoming increasingly challenging, necessitating the development of systems capable of providing personalized health insights based on accurate root-cause analysis. Methods: In this study, we compared predicted symptoms derived from objective TCM organ pattern analysis using the Nadifit pulse diagnosis system with subjective clinical assessments of patients' symptoms. TCM organ patterns were determined based on the combination of five elements and their Yin/Yang energy levels. Pulse signals were collected from 105 individuals and compared with clinical evaluations of patients' symptoms. Results: The analysis revealed a high level of agreement between the clinical assessment of symptoms and the predicted organ patterns, with a Kappa coefficient of 0.82. This suggests that objective pulse analysis effectively identifies root causes, aligning with subjective assessments. Conclusions: The findings of this study underscore the validity and reliability of objective pulse analysis in diagnosing health conditions. By demonstrating significant agreement with subjective clinical assessments, this method provides a promising avenue for enhancing diagnostic accuracy and facilitating personalized treatment strategies based on individual health profiles.

Effects of Calorie Restriction on Preserving Male Fertility Particularly in a State of Obesity.

Highlight the importance of exploring nutritional interventions that could be applied as alternative or supplementary therapeutic strategies to enhance men's fertility. Lifestyle choices haveprompted extensive discussions regarding its implications and applications as a complementary therapy. The growing concern over the decline in sperm quality underscores the urgency of investigating these alternative interventions. Calorie restriction (CR) has emerged as a promising strategy to improve male fertility. The efficacy of CR depends on factors like age, ethnicity and genetics. Clinical studies, such as CALERIE,have shown an improvement in serum testosterone level and sexual drive in men with or without obesity. Additionally, CR has been shown to positively impact sperm count and motility; however, its effects on sperm morphology and DNA fragmentation remain less clear, and the literature has shown discrepancies, mainly due to the nature of technically dependent assessment tools. The review advocates a personalized approach to CR, considering individual health profiles to maximize its benefits. It underscores the need for routine, accessible diagnostic techniques in male reproductive health. Itsuggests that future research should focus on personalized dietary interventions to improve male fertility and overall well-being in individuals with or without obesity and unravel CR's immediate and lasting effects on semen parameters in men without obesity.

Comparing lycopene's impact on mortality in adults with or without obesity.

Objective: Given lycopene's anti-inflammatory and antioxidant properties, we investigated its mortality impact in individuals with and without obesity, confirming distinct effects. Methods: This study analyzes the National Health and Nutrition Examination Survey (NHANES) data from 2003-2006 and 2017-2018, linking lycopene levels to all-cause and cardiovascular mortality. Using various statistical methods, three models are sequentially adjusted for confounders, investigating the lycopene-outcome relationship. Results: We studied 11 737 adults for 162 months and found 1537 all-cause deaths (13.1%) and 443 cardiovascular deaths (3.8%). For those without obesity, serum lycopene had an "L" shape relationship with all-cause mortality, being harmful at very low levels but protective above a certain threshold. It consistently protects against cardiovascular mortality. In individuals with obesity, the relationship with all-cause mortality formed a "U" shape, with increased risk at very low and very high lycopene levels and protection in the middle range. Cardiovascular mortality showed a similar pattern in individuals with obesity. Interestingly, dietary lycopene intake had protective effects in both groups. Conclusion: This study reveals that lycopene exhibits distinct associations with all-cause and cardiovascular mortality in populations with or without obesity, emphasizing the importance of considering individual health profiles when assessing its benefits.

IntelliCare: Integrating IoT and Machine Learning for Remote Patient Monitoring in Healthcare: A Comprehensive Framework

The development of smart health monitoring systems has emerged as a consequence of the integration of Internet of Things (IoT) and Machine Learning (ML) technologies within the healthcare sector. This transformation has significantly reshaped patient care methodologies, shifting from traditional approaches to electronic healthcare systems. Leveraging IoT technology fosters a contemporary medical device ecosystem, fostering seamless communication among healthcare professionals, patients, and medical devices. Through the deployment of IoT devices, encompassing sensors and transmitters, coupled with Machine Learning algorithms, various applications have arisen, spanning from remote patient monitoring to real-time health assessment during ambulance transit to medical facilities. This proposed framework aims to monitor essential physiological parameters including body temperature, blood pressure, heart rate, sweat analysis, glucose levels, ECG, EEG, and pulse oximetry, transmitting pertinent data for tailored processing and analysis. Implantable IoT devices serve as conduits for wireless communication, data storage, centralized computation, and portable processing, facilitating connectivity among sensors, GPS-enabled ambulances, medical practitioners, and patients. To mitigate potential health risks, sensors are equipped with Machine Learning capabilities to promptly assess illness severity and recommend appropriate interventions, potentially triggering automated alerts to healthcare providers. This seamless exchange of information via IoT and wireless networks enables rapid communication between doctors and patients, facilitating personalized medical recommendations, prescription management, and hospital selection based on individual health profiles.

Leading Causes of Chronic Kidney Disease Among Dialysis Patients in Al-Baha Region, Saudi Arabia.

Introduction Chronic kidney disease (CKD) and its progression to end-stage renal disease (ESRD) represent a growing health concern globally, with hypertension and diabetes commonly identified as primary etiological factors. This study evaluates the demographic and health profiles of individuals undergoing dialysis treatment in the Al-Baha region of Saudi Arabia, aiming to identify the predominant causes of ESRD and the associated socioeconomic and healthcare-related factors. Methodology This cross-sectional study analyzed data from patients receiving dialysis in Al-Baha. We assessed variables including etiology of ESRD, demographic data, presence of comorbid conditions, initial symptoms, and pre-dialysis health care engagement. Statistical analysis focused on the prevalence and correlations between the different variables and ESRD. The study also examined patients' educational background and employment status to ascertain ESRD's socioeconomic impact. Results The study found hypertension and diabetes as the leading causes of ESRD. Unknown etiologies accounted for 10.1% of cases, highlighting an area for further research. Notably, coronavirus disease 2019 (COVID-19) and cardiogenic shock emerged as potential new contributors, each representing 1.7% of cases. Most patients resided in urban areas, with the largest age group being 46 to 55 years. Men had a higher prevalence of ESRD than women. Low educational attainment was significant among patients, and unemployment due to ESRD was prevalent, pointing towards the need for vocational support. Clinical findings revealed late referrals to nephrologists, with a substantial proportion of diagnoses occurring in emergency settings. Family history suggested a higher-than-expected genetic component of CKD in the region. Conclusion The study confirms hypertension and diabetes as principal contributors to ESRD in Al-Baha while also pointing to the emergence of COVID-19 as a potential risk factor. Socioeconomic factors, including educational and employment status, are critical to patient management and outcomes. The high percentage of unknown etiologies and familial CKD prevalence warrants additional research. Improving early detection, enhancing patient education, and fostering timely nephrology consultations could mitigate the progression to ESRD and enhance patient quality of life.

Health profile of individuals participating in a public exercise program

Health profile of individuals participating in a public exercise program

Developing a Personalized Meal Recommendation System for Chinese Older Adults: Observational Cohort Study.

China's older population is facing serious health challenges, including malnutrition and multiple chronic conditions. There is a critical need for tailored food recommendation systems. Knowledge graph-based food recommendations offer considerable promise in delivering personalized nutritional support. However, the integration of disease-based nutritional principles and preference-related requirements needs to be optimized in current recommendation processes. This study aims to develop a knowledge graph-based personalized meal recommendation system for community-dwelling older adults and to conduct preliminary effectiveness testing. We developed ElCombo, a personalized meal recommendation system driven by user profiles and food knowledge graphs. User profiles were established from a survey of 96 community-dwelling older adults. Food knowledge graphs were supported by data from websites of Chinese cuisine recipes and eating history, consisting of 5 entity classes: dishes, ingredients, category of ingredients, nutrients, and diseases, along with their attributes and interrelations. A personalized meal recommendation algorithm was then developed to synthesize this information to generate packaged meals as outputs, considering disease-related nutritional constraints and personal dietary preferences. Furthermore, a validation study using a real-world data set collected from 96 community-dwelling older adults was conducted to assess ElCombo's effectiveness in modifying their dietary habits over a 1-month intervention, using simulated data for impact analysis. Our recommendation system, ElCombo, was evaluated by comparing the dietary diversity and diet quality of its recommended meals with those of the autonomous choices of 96 eligible community-dwelling older adults. Participants were grouped based on whether they had a recorded eating history, with 34 (35%) having and 62 (65%) lacking such data. Simulation experiments based on retrospective data over a 30-day evaluation revealed that ElCombo's meal recommendations consistently had significantly higher diet quality and dietary diversity compared to the older adults' own selections (P<.001). In addition, case studies of 2 older adults, 1 with and 1 without prior eating records, showcased ElCombo's ability to fulfill complex nutritional requirements associated with multiple morbidities, personalized to each individual's health profile and dietary requirements. ElCombo has shown enhanced potential for improving dietary quality and diversity among community-dwelling older adults in simulation tests. The evaluation metrics suggest that the food choices supported by the personalized meal recommendation system surpass autonomous selections. Future research will focus on validating and refining ElCombo's performance in real-world settings, emphasizing the robust management of complex health data. The system's scalability and adaptability pinpoint its potential for making a meaningful impact on the nutritional health of older adults.

PREDICTIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR DETECTION OF INCIDENT HEART FAILURE USING MULTI-CENTRE DATA

Objective: To mitigate the health and economic burden associated with heart failure (HF), primary prevention is of utmost importance. To effectively address the diverse range of individual health profiles, prediction models must be trained and validated on a wide range of population-based cohorts. We evaluated the predictive performance of Survival Gradient Boosting (SGB) and Coxnet (CN) for the detection of incident HF. In addition, we compared the models’ performance trained only on a single-centre cohort with those trained on diverse multi-centre cohorts. Design and method: We utilized 6 cohorts from the Heart ‘omics’ in AGEing (HOMAGE) project to evaluate two computational approaches. The dataset consists of 31,382 individuals (mean age 66 ± 10 years old), free of HF at the baseline. During a median follow-up of 5.36 years, 1,130 non-fatal HF events occurred. SGB is a non-linear machine learning method based on training regression trees with the objective of optimizing Cox partial likelihood. Coxnet is a standard linear Cox proportional hazard model, regularized by both L1 and L2 norms. To estimate the predictive accuracy of a single-centre model on a specific test cohort, we averaged the performance of single models trained on remaining cohorts one at a time. To develop models in multi-centre settings, we used 5 cohorts in every iteration as a training set and the remaining 1 cohort as a test set. We used 33 features, including clinical, ECG, and biochemical data. Results: Overall, multi-centre models achieved a higher c-index than those trained on single cohorts (Figure). For example, in the FLEMENGHO cohort, multi-centre SGB achieved 0.819, while only 0.664 in a single-centre setting. Additionally, non-linear SGB achieved a considerably higher c-index than linear CN in all but one cohort, especially in multi-centre settings, for example in the HVC cohort (SGB 0.689, CN 0.614). Conclusions: With a greater number and variety of training cohorts, the model learns a wider range of specific individual health characteristics. Flexible machine learning algorithms such as gradient boosting can be used to capture these diverse distributions and produce more precise, personalized models for the prediction of adverse events.

Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches.

The COVID-19 pandemic has been widely spread and affected millions of people and caused hundreds of deaths worldwide, especially in patients with comorbilities and COVID-19. This manuscript aims to present models to predict, firstly, the number of coronavirus cases and secondly, the hospital care demand and mortality based on COVID-19 patients who have been diagnosed with other diseases. For the first part, I present a projection of the spread of coronavirus in Mexico, which is based on a contact tracing model using Bayesian inference. I investigate the health profile of individuals diagnosed with coronavirus to predict their type of patient care (inpatient or outpatient) and survival. Specifically, I analyze the comorbidity associated with coronavirus using Machine Learning. I have implemented two classifiers: I use the first classifier to predict the type of care procedure that a person diagnosed with coronavirus presenting chronic diseases will obtain (i.e. outpatient or hospitalised), in this way I estimate the hospital care demand; I use the second classifier to predict the survival or mortality of the patient (i.e. survived or deceased). I present two techniques to deal with these kinds of unbalanced datasets related to outpatient/hospitalised and survived/deceased cases (which occur in general for these types of coronavirus datasets) to obtain a better performance for the classification.

AI-Driven Metabolmics for Precision Nutrition: Tailoring Dietary Recommendations based on Individual Health Profiles.

AI-Driven Metabolmics for Precision Nutrition: Tailoring Dietary Recommendations based on Individual Health Profiles.

Ambient particulate matter levels and health profile in residents of Choba and Mgbuoba areas of Rivers State: A cross-sectional study

ABSTRACT The study investigated the health profile of individuals exposed to particulate matter (Black) soot in Choba and Mgbuoba localities of Port Harcourt. The analysis was executed with the aid of 100 respondents (50 from Choba and 50 from Mgbuoba). A structured questionnaire with the outlined elements was utilized during data collection for the study: manner of living, type of occupation, environmental exposure, known health issues, fitness and cleanliness, and Key Informant Interviews (KII) were exploited to gain pertinent data from health professionals. A hand-held Kestrel weather tracker was employed to ascertain the wind speed, temperature, and relative humidity of the study areas while A Met One Instrument, Inc. Aerosol Mass Monitor Model GT–531 was utilized to estimate total suspended particulates. Biochemical parameters such as lipid profile, renal markers, and liver markers activities were analyzed from blood samples obtained from the subjects using spectrophotometric methods. The air quality profile of Mgbuoba showed significant (p < .05) increment in total suspended particulate matter, total suspended particulate matter (TSPM), particulate matter that is 10 microns in diameter or less (PM10), and particulate matter that is 2.5 microns in diameter or less (PM2.5) from 6 a.m. to 8 a.m. compared with Choba. The increment in Total suspended particulate matter, PM10, and PM2.5 levels were beyond the 24-hour PM10 and PM2.5 recommended level by the World Health Organization (WHO). The alanine transaminase (ALT) and low density lipoproptein (LDL) levels of Choba and Mgbuoba residents were beyond the reference limits set by the Medical Council of Canada. The study showed that residents in Mgbuoba in Port Harcourt may be more at risk and have higher vulnerability to the undesirable outcomes associated with exposures to these air contaminants. A rising and worrisome trend in respiratory and related conditions among less mobile residents call for emergency response to emission of black soot in the area. Implications: The study investigated the health profile of individuals exposed to particulate matter (Black) soot in Choba and Mgbuoba localities of Port Harcourt. This outcome clearly shows that these air pollutants are higher in concentrations early in the morning and their concentrations decreased as the day went by. Therefore, by implication, the residents in these areas may be at high risk of exposure to these air pollutants from 6 a.m. to 8 a.m. The present study has demonstrated that these air pollutants are more prominent very early in the mornings and variations in meteorological conditions at a particular time, and anthropogenic activities might have contributed to the elevations in the concentration of TSPM, PM10, and PM2.5 in Choba and Mgbuoba.

Health Care Needs and Utilization Among New Yorkers with Criminal Justice System Involvement

Research ObjectivePeople with a history of incarceration have high rates of physical and behavioral health conditions as well as increased hospitalizations and emergency department visits. This research extends the existing literature about the health profile of individuals with a history of justice involvement by examining patterns of Medicaid utilization and costs.Study DesignWe linked New York State Medicaid and New York City Department of Correction Data from 2012–2016 and examined patterns of jail utilization, health care utilization (including inpatient, emergency department, and outpatient use) and health care cost by categories of service, as well as the prevalence of chronic physical and mental health disorders. We selected two sets of comparisons, any jail contact vs. no jail contact and high jail contact vs. low jail contact.Population StudiedIndividuals with at least one jail admission in 2012 were defined as having any jail contact. Individuals with only one jail admission between 2012–2016 are defined as having low jail contact. Those who were in the top 10 percentile of number of jail admissions are defined as having high jail contact. The no jail contact group is defined as Medicaid recipients matched with individuals with any jail contact on age, sex, race/ethnicity, Medicare status and number of Medicaid enrollment months.Principal FindingsComparing to individuals with no jail contact, any jail contact was associated with a higher prevalence of both chronic physical and mental health conditions; those with any jail contact had a doubling in the risks of mortality and of health care resource use; the average number of hospitalizations was 4 times higher among people with any jail contact; people with jail contact had Medicaid costs that were 3 to 7 times greater, with inpatient costs largely driving these differences for people with the most frequent jail contact. Compared to individuals with low jail contact, individuals with high jail contact had drastically higher prevalence of both chronic physical and mental health conditions, triple the number of hospitalizations, and quadruple the number of emergency department visits. The annual inpatient expenditure of an individual with high jail contact is $12 k higher than that of an individual with low jail contact; total Medicaid expenditures of high contact individuals averages over $33 k per year and are twice as high as those of low contact individuals.ConclusionsThe prevalence of physical and mental health conditions was very high among individuals with high jail contact, as were their health care costs. Only a small fraction of costs for this group were for outpatient primary care, behavioral health treatment, and case management that could help to address these needs and prevent hospitalization.Implications for Policy or PracticeThese findings highlight the need for more effective health care engagement strategies for people with a history of incarceration. Specifically, preventative, primary care and behavioral health care services — particularly outpatient substance use services — that are tailored to the needs of this population could lead to more effective health care engagement, reduced health care costs, and improved health and well‐being for individuals who have had some contact with NYC jails.Primary Funding SourceThe Robert Wood Johnson Foundation.

Oral health profile of postbariatric surgery individuals: A case series.

ObjectivesTo describe the oral health profile of individuals who had undergone gastric bypass surgery (GBP) or sleeve gastrectomy (SG) to generate hypotheses for further studies.Material and MethodsFourteen individuals treated with GBP or SG surgery ≥ 2 years ago and with observed and/or perceived oral problems were recruited to a case series. The documentation included clinical and radiographic examinations, biomedical sampling, and self‐reported diet and questionnaires. The results are presented descriptively. Results: The age range was 31 to 66 years and all had a BMI > 25 (range 25.4–44.7). Only four participants were fully dentate. Eleven out of 14 individuals exhibited severe decay. A majority had poor oral hygiene and high bacterial counts. The flow rates of unstimulated saliva were extremely low and hyposalivation was present in ten of the fourteen cases. Most perceived several oral health problems, such as chewing difficulty and tooth hypersensitivity.ConclusionsIndividuals who had undergone GBP or SG surgery had poor clinically diagnosed oral health and perceived oral health problems. Longitudinal studies are needed to monitor the patients' oral health, from before bariatric surgery to long‐term postoperatively.

COVID-19 Pandemi Sürecinde Yoganın Bireylerin Sağlık Profili, Depresyon ve Anksiyete Düzeyleri Üzerinde Etkileri: Randomize Çalışma

Objective: With the effect of the pandemic and a sedentary lifestyle, it is thought that physical health may be negatively affected. Moreover, individuals also experience negative feelings like stress and anxiety due to spending a long time at home, social isolation, changes in daily routines and constant exposure to news stories on the media about the coronavirus. Yoga-based exercises are interventions that are effective on both health and stress. This study was conducted for the purpose of determining the effects of yoga on the health profile, depression and anxiety levels of individuals in the coronavirus disease-2019 (COVID-19) pandemic process. Material and Methods: The data in this study were collected by an online survey database (Google Forms) in entire Turkey. A total of 374 individuals were reached, while 137 of these individuals stated that they did yoga-based exercises (Yoga Group), and 237 said they did not take part in any physical activity (Sedentary Group). The questionnaire was open to everyone over the age of 18, and the yoga instructors were asked to share with their groups in order to reach those who exercise yogabased. The study assessed the participants' general health status by using the Nottingham Health Profile (NHP), depression by the Beck Depression Inventory and anxiety by the Spielberger State-Trait Anxiety Inventory. Result: In terms of the health profiles, there were significant differences in those who did yoga (Yoga Group) and the sedentary individuals (Sedentary Group) in all parameters of NHP except for the sleep and social isolation parameters (p<0.05). There were also significant differences between the groups in terms of their depression and anxiety levels (p<0.05). Besides, no difference was found between those who do yoga 0- 14 days and sedentary group. Conclusion: These findings show that, in the COVID-19 pandemic period, yoga has positive effects on individual's health profile, depression and anxiety levels.

Substance use patterns and health profiles among US adults who use opioids, methamphetamine, or both, 2015-2018

BackgroundMethamphetamine use, with and without opioids, has increased substantially, but little is known about the sociodemographic characteristics, substance use patterns, or health profiles of individuals who use methamphetamine. To design effective public health interventions, health care professionals and policymakers need data describing individuals who are using methamphetamine in the midst of the opioid crisis. MethodsWe used 2015–2018 data from the National Survey on Drug Use and Health and included non-elderly adults aged 18–64 years. We categorized respondents into three groups: use of opioids without methamphetamine use, use of methamphetamine without opioid use, or use of both opioids and methamphetamine. Multiple logistic regression models controlling for sociodemographic factors were used to compare substance use characteristics and measures of individual health between the three groups. ResultsPeople who used any methamphetamine were more likely to be unstably housed, low-income, and live in rural areas. Use of both opioids and methamphetamine was associated with a 132 % higher prevalence of injection needle use, and a nearly twofold higher prevalence of viral hepatitis compared with opioid use alone. One third of individuals reporting use of both opioids and methamphetamine had a severe mental illness, a 55 % higher prevalence than those using opioids alone. ConclusionsIndividuals who used opioids and methamphetamine had more complex substance use and health profiles than individuals who used opioids alone. These findings suggest public health and harm reduction approaches designed to address opioid use remain important in an era of rising methamphetamine use.

P1212PREDICTORS OF QUALITY OF LIFE IN PERITONEAL DIALYSIS: A SINGLE-CENTER EXPERIENCE

Abstract Background and Aims Quality of life (QoL) is an important indicator of quality of healthcare. Measuring QoL and its correlates for peritoneal dialysis (PD) patients is very important for intervention and clinical decision-making. Being a technique performed by the patients themselves, their perspective of health related QoL is extremely important. The EuroQol questionnaire evaluates 5 QoL dimensions (mobility, self-care, usual activities, pain/ discomfort and anxiety/ depression) and includes a visual analogue scale (VAS) rated 0-100% that provides a quantitative measure of the patients’ perception of their overall health. Method Data from 69 patients on peritoneal dialysis at our center were collected using EQ-5D-5L EuroQol questionnaire. Health state index (HSI) scores were calculated from individual health profiles using the Spanish value set (maximum score 1). Additional clinical and laboratory data was collected from the patient’s medical files. Results Mean age of the population studied was 55,2 ± 14,0 years, and 66,7% (n=46) were male. Most patients were on PD &amp;gt; 1 year (62,3%, n=43) and performing automated peritoneal dialysis (APD) (52,2%, n=36). Mean HSI was 0,88 ± 0,15 and mean VAS score was 75,1 ± 18,7. Patients with weekly Kt/V ≥ 1,7 scored on average 0,09 points higher on the HSI (p=0,070) and 13,34 points higher in VAS (p=0,019), compared to patients with weekly Kt/V &amp;lt; 1,7. Patients on PD &amp;gt; 1 year scored on average 0,09 points lower on the HSI (p=0,017), mainly due to higher levels of anxiety/depression, and 11,9 points lower in VAS (p=0,005) than those &amp;lt; 1 year. Age did not significantly influence QoL, even when comparing patients &amp;gt; 70 years with those &amp;lt; 70 years old (HSI 73.0 ± 15.5 vs 75.4 ± 19.3, p=0.710 / VAS 0.82 ± 0.3 vs 0.89 ± 0.1, p=0.175). Likewise, when comparing continuous ambulatory peritoneal dialysis (CAPD) to APD, there was no significant difference in the scores of QoL. Gender, diabetes mellitus, arterial hypertension, cancer and infectious events related to PD in the last year did not influence QoL. Residual diuresis as an isolated factor did not significantly influence QoL scores. However, as it is a fundamental contributor to Kt/V, it may be indirectly associated with better QoL. A more detailed analysis was not possible as the number of patients with Kt/V ≥ 1.7 and no residual diuresis was, as expected, very low (n=4). Conclusion Perceived QoL in the elderly when compared to younger patients in PD was not inferior in our study, showing this option should be discussed individually with each patient. Our results revealed time in PD negatively influences patients’ perception of their own health, which may be due to patient’s burden and exhaustion, and eventually lead to technique related complications. Kt/V ≥ 1,7 was related to better QoL, either because higher dialysis adequacy leads to better QoL, or because patients who feel better have less tendency to neglect the technique. Finally, we also found type of PD (CAPD vs APD) to have no significant influence on QoL, reinforcing the idea that it should be discussed and adapted to each individual patient.