The rapid development of large language models (LLMs), including ChatGPT, Gemini, and Copilot, has led to their increasing use in health communication and patient education. However, their growing popularity raises important concerns about whether the language they generate aligns with recommended readability standards and patient health literacy levels. This review synthesizes evidence on the readability of medical information generated by chatbots using established linguistic readability indices. A comprehensive search of PubMed, Scopus, Web of Science, and Cochrane Library identified 4209 records, from which 140 studies met the eligibility criteria. Across the included publications, 21 chatbots and 14 readability scales were examined, with the Flesch–Kincaid Grade Level and Flesch Reading Ease being the most frequently applied metrics. The results demonstrated substantial variability in readability across chatbot models; however, most texts corresponded to a secondary or early tertiary reading level, exceeding the commonly recommended 8th-grade level for patient-facing materials. ChatGPT-4, Gemini, and Copilot exhibited more consistent readability patterns, whereas ChatGPT-3.5 and Perplexity produced more linguistically complex content. Notably, DeepSeek-V3 and DeepSeek-R1 generated the most accessible responses. The findings suggest that, despite technological advances, AI-generated medical content remains insufficiently readable for general audiences, posing a potential barrier to equitable health communication. These results underscore the need for readability-aware AI design, standardized evaluation frameworks, and future research integrating quantitative readability metrics with patient-level comprehension outcomes.

Objectives: Bone mineral density (BMD) assessment, the gold standard for diagnosing osteoporosis, does not account for bone quality and fracture susceptibility. Trabecular bone score (TBS) adds value to traditional densitometry. No studies have been conducted in the Polish population to date to confirm the association between TBS and fracture occurrence. This study aimed to evaluate the TBS derived from lumbar spine (L1-L4) dual-energy X-ray absorptiometry (DXA) scans in Polish women aged 40-76 years, both with and without osteoporotic fractures. The relationship between TBS, fracture risk (assessed by FRAX and TBS-adjusted FRAX), and BMD at the lumbar spine, femoral neck, and total hip was investigated. Methods: A total of 933 Caucasian women (760 without fracture and 173 with fracture) who underwent DXA examinations (Hologic Discovery A) between 2022 and 2024 were included. Lumbar TBS, BMD, and clinical fracture risk factors were analyzed, excluding subjects with scan artefacts or extreme BMI. Group differences were assessed using t-tests and chi-square tests. Pearson correlation was used to evaluate associations between TBS, age, and BMI. Logistic regression models assessed TBS and BMD as fracture discrimination, and model performance was compared using the Akaike Information Criterion (AIC) and the area under the receiver operating characteristic (ROC) curve (AUC). Results: TBS values were significantly lower in the fracture group (p < 0.001). TBS demonstrated negative correlations with age (r ≈ -0.36) and BMI (r ≈ -0.14). Low TBS values (≤1.23) were associated with the highest fracture prevalence (28.8%) and a threefold increased risk compared to high TBS (odds ratio = 3.0). Each one standard-deviation decrease in BMD or TBS T-score increased fracture risk by 56-67% (both p < 0.001). Models combining TBS and BMD improved discrimination, as indicated by higher AUC and lower AIC, with TBS remaining an independent predictor. In subgroups with osteopenia or osteoporosis, TBS retained statistical significance. Conclusions: TBS combined with BMD effectively discriminates fracture risk in Polish women and offers superior diagnostic accuracy compared to BMD alone. Integrating TBS with BMD enhances fracture accuracy. Routine assessment of TBS may improve clinical management of osteoporosis. Prospective studies are needed to confirm its long-term predictive value.

In contrast to low-density lipoproteins which are atherogenic, high-density lipoproteins (HDL) have been conceptualized as beneficial modulators of adverse pathophysiological phenomena along arterial walls. The HDLs are characterized by highly complex and varied molecular cargoes that include apoproteins, enzymes, microRNAs, bioactive lipids and phospholipids, components of complement, and immune factors, among others. These cargo components determine its functionality. Despite the findings of Mendelian inheritance studies which suggest that HDL is not causal in the pathway for atherogenesis, experiments with HDLs show that it can drive reverse cholesterol transport and antagonize inflammation, oxidation, thrombosis, platelet aggregation, endothelial progenitor cell mobilization, potentiate immunity, foster communication between different cell and tissue types, and function as a crucial apoprotein donor amongst the various lipoproteins. These functions are understandably viewed as beneficial and antagonize pathophysiology. Secondary to the complexity of its proteome and lipidome, HDL functionality is profoundly responsive to the metabolic and genetic backgrounds of individuals. Even its size and lipidation status can influence its functionality. As part of the acute phase response, critical antioxidative moieties can be replaced by such acute phase reactants as serum amyloid A and pro-oxidative enzymes. The functionality of HDL is influenced by chronic kidney disease, coronary artery disease, acute myocardial infarction, obesity, insulin resistance, metabolic syndrome, diabetes mellitus, and cancer. Herein we describe many of the alterations in HDL constitution and the resulting changes in functional capacity that can be observed. A unifying theme characterizing these disease states is that they all heighten systemic inflammatory tone and potentiate a pro-oxidative state. These changes clearly associate with profound changes in the functionality and behavior of HDL particles. We are only beginning to comprehend the extraordinary complexity and range of biochemical functions, both beneficial and injurious, that this lipoprotein can regulate. Hence it was extremely premature to think that simply raising HDL cholesterol in serum would beneficially influence cardiovascular morbidity and mortality. We have a long way to go before we develop a more comprehensive and potentially therapeutically relevant understanding of how to better harness its potential for antagonizing disease and block its ability to participate in and adversely influence the course of disease.

AI-powered chatbots, using Large Language Models, may effectively answer questions from patients with hypertension, providing responses that are accurate, empathetic, and easy to read. This study evaluates the performance of three such chatbots in delivering quality responses. Due to staffing shortages within the healthcare system, chatbots may be a viable alternative for diagnosing or educating patients about diseases, particularly chronic conditions most prevalent in the community, such as hypertension. One hundred questions were randomly selected from the Reddit forum r/hypertension and submitted to three publicly available chatbots (ChatGPT-3.5, Microsoft Copilot, Gemini), anonymized as A, B, and C. Two independent medical professionals assessed the accuracy and empathy of their responses using Likert scales. Additionally, 300 responses were analyzed with the WebFX readability tool to measure various readability indices. This tool evaluates the text based on various readability scales, including the Flesch Kincaid Reading Ease Score, Flesch Kincaid Grade Level, Gunning Fog Score, Smog Index, Coleman Liau Index , and Automated Readability Index. In total, 300 responses were evaluated. Our findings indicate that Chatbot A consistently produces the most extended responses compared to other chatbots. Utilizing the Flesch-Kincaid Reading Ease scale, it is evident that all chatbot responses are crafted with advanced language. Notably, Chatbot’s A responses are the most challenging to comprehend. The Flesch-Kincaid Grade Level results further reveal that Chatbot’s A responses are the most sophisticated, employing language typically associated with college-level writing. Chatbot B and Chatbot C achieved identical scores regarding the Gunning Fog Score and SMOG Index. However, Chatbot A attained the highest scores again, underscoring its propensity to generate highly advanced responses. The Coleman-Liau Index and Automated Readability Index scores also corroborate the high reading comprehension level required for Chatbot’s A responses, highlighting their complexity and advanced nature. The readability indicator values for all scales significantly differed among chatbots (Table 1). Figure 1 presents a pie chart illustrating the distribution of question categories and subcategories. The study indicates that while all chatbots can produce professional responses, their readability varies significantly. These findings underscore the potential of AI chatbots in patient education. However, they also highlight the urgent need for further optimization to enhance the comprehensibility of their outputs. While high readability levels are suitable for medical professionals, they can pose challenges for laypersons who may need to become more familiar with medical terminology. This discrepancy underscores the need for future research to evaluate and optimize the readability of chatbot responses.Tab. 1Readability comparisonFig. 1Frequency of questions

Background. The coronavirus disease 2019 (COVID-19) pandemic has significantly accelerated the development and use of new healthcare technologies. While younger individuals may have been able to quickly embrace virtual advancements, older adults may still have different needs in terms of health communication. Objectives. To identify areas of interest and preferred sources of information related to the COVID-19 pandemic among older adults and to verify their eHealth competencies. Materials and methods. The study was conducted between February 2022 and July 2022. It included listeners from the University of the Third Age (U3A) and younger students. Both groups received information about the HealthBuddy+ chatbot, a questionnaire that addressed respondents' interests about COVID-19, and the PL-eHEALS (eHealth Literacy Scale) questionnaire to measure their eHealth competencies. Results. There were 573 participants in the study (U3A listeners - 303 participants, median age: 73 years (interquartile range (IQR): 69-77); young adult students - 270, median age: 24 years (IQR: 23-24). The primary source of information about COVID-19 for older adults was television (84.5%), and for younger adults, internet (84.4%). Among the older adults, only 17% ever interacted with a chatbot (younger adults - 78% respectively), and 19% considered it a trustworthy source of information on COVID-19 compared to 79% of younger respondents. Older adults and younger adults in our study were most interested in COVID-19 treatment methods (45.5% and 69.3%, respectively), symptoms of the disease (36.6% and 35.2%, respectively) and chronic diseases coexisting with COVID-19 (35.0% and 51.5%, respectively). However, their eHealth competencies were generally low (median (Me): 34; IQR: 30-39) compared to younger adults (Me: Conclusions. Health education for older adults should be appropriately tailored to their current needs and differentiated. The level of eHealth competencies of older adults suggests that much work remains to narrow the gap between the eHealth competencies of the younger and older generations.

Chronic kidney disease patients appear to be predisposed to heart rhythm disorders, including atrial fibrillation/atrial flutter, ventricular arrhythmias, and supraventricular tachycardias, which increase the risk of sudden cardiac death. The pathophysiological factors underlying arrhythmia and sudden cardiac death in patients with end-stage renal disease are unique and include timing and frequency of dialysis and dialysate composition, vulnerable myocardium, and acute proarrhythmic factors triggering asystole. The high incidence of sudden cardiac deaths suggests that this population could benefit from implantable cardioverter-defibrillator therapy. The introduction of implantable cardioverter-defibrillators significantly decreased the rate of all-cause mortality; however, the benefits of this therapy among patients with chronic kidney disease remain controversial since the studies provide conflicting results. Electrolyte imbalances in haemodialysis patients may result in ineffective shock therapy or the appearance of non-shockable underlying arrhythmic sudden cardiac death. Moreover, the implantation of such devices is associated with a risk of infections and central venous stenosis. Therefore, in the population of patients with heart failure and severe renal impairment, periprocedural risk and life expectancy must be considered when deciding on potential device implantation. Harmonised management of rhythm disorders and renal disease can potentially minimise risks and improve patients’ outcomes and prognosis.

Background: Brachial aortic Pulse Wave Velocity (baPWV) and bone mineral density (BMD) are important indicators of cardiovascular health and bone strength, respectively. However, the gender-specific association between baPWV and BMD remains unclear. The aim of our study is to evaluate the relationship between baPWV and BMD in men and women populations Methods: A comprehensive search was conducted in electronic databases for relevant studies published between the 1th and 30rd of April 2023. Studies reporting the correlation between baPWV and BMD in both males and females were considered. A random-effects model was used to calculate pooled correlation coefficients (r). Results: Relevant data for both genders were found in six articles. In all publications included in the metaanalysis, the total number of studied individuals was 3800, with 2054 women and 1746 men. Pooled correlation coefficient was -0,24 (95 % CI: -0.34; -0.15) in women population, and -0.12 (95 %CI: -0.16, -0.06) in men. Conclusions: Based on the published data, we found that baPWV is negatively correlated with bone density in women. However, in men we do not find such a relationship. These findings suggest the importance of considering gender-specific factors when assessing the cardiovascular and bone health relationship.

AI-powered chatbots, using Large Language Models, may effectively answer questions from patients with hypertension, providing responses that are accurate, empathetic, and easy to read. This study evaluates the performance of three such chatbots in delivering quality responses. One hundred questions were randomly selected from the Reddit forum r/hypertension and submitted to three publicly available chatbots (ChatGPT-3.5, Microsoft Copilot, Gemini), anonymized as A, B, and C. Two independent medical professionals assessed the accuracy and empathy of their responses using Likert scales. Additionally, 300 responses were analyzed with the WebFX readability tool to measure various readability indices. In total, 300 responses were evaluated. Chatbot A generated the most extensive responses, with an average of 13 sentences per reply, while Chatbot B had the shortest replies. Chatbot C achieved the highest score on the Flesch Reading Ease Scale, indicating better readability, while Chatbot A scored the lowest. Other readability metrics, including the Flesch-Kincaid Grade Level, Gunning Fog Score, and others, also showed significant differences among the chatbots, reflecting variability in readability. The study indicates that while all chatbots can produce professional responses, their readability varies significantly. These findings underscore the potential of AI chatbots in patient education. However, they also highlight the urgent need for further optimization to enhance the comprehensibility of their outputs.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

Background: Chatbots using the Large Language Model (LLM) generate human responses to questions from all categories. Due to staff shortages in healthcare systems, patients waiting for an appointment increasingly use chatbots to get information about their condition. Given the number of chatbots currently available, assessing the responses they generate is essential. Methods: Five chatbots with free access were selected (Gemini, Microsoft Copilot, PiAI, ChatGPT, ChatSpot) and blinded using letters (A, B, C, D, E). Each chatbot was asked questions about cardiology, oncology, and psoriasis. Responses were compared to guidelines from the European Society of Cardiology, American Academy of Dermatology and American Society of Clinical Oncology. All answers were assessed using readability scales (Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level and Dale-Chall Score). Using a 3point Likert scale, two independent medical professionals assessed the compliance of the responses with the guidelines. Results: A total of 45 questions were asked of all chatbots. Chatbot C gave the shortest answers, 7.0 (6.0 - 8.0), and Chatbot A the longest 17.5 (13.0 - 24.5). The Flesch Reading Ease Scale ranged from 16.3 (12.2 - 21.9) (Chatbot D) to 39.8 (29.0 - 50.4) (Chatbot A). Flesch-Kincaid Grade Level ranged from 12.5 (10.6 - 14.6) (Chatbot A) to 15.9 (15.1 - 17.1) (Chatbot D). Gunning Fog Scale Level ranged from 15.77 (Chatbot A) to 19.73 (Chatbot D). Dale-Chall Score ranged from 10.3 (9.3 - 11.3) (Chatbot A) to 11.9 (11.5 - 12.4) (Chatbot D). Conclusion: This study indicates that chatbots vary in length, quality, and readability. They answer each question in their own way, based on the data they have pulled from the web. Reliability of the responses generated by chatbots is high. This suggests that people who want information from a chatbot need to be careful and verify the answers they receive, particularly when they ask about medical and health aspects.

Abstract Background Amidst the backdrop of staff shortages in healthcare systems, patients and their families are increasingly turning to chatbots, powered by Large Language Models (LLMs), for information about their medical conditions. These AI-driven chatbots, capable of generating human-like responses across a broad range of topics, have become a prevalent tool in the healthcare landscape. Given the proliferation of these chatbots, it is crucial to evaluate the quality and accuracy of the responses they provide. Methods We selected five freely accessible chatbots (Bard, Microsoft Copilot, PiAI, ChatGPT, and ChatSpot) for our study. These chatbots were posed questions spanning three medical fields: cardiology, cardio-oncology, and cardio-rheumatology. The responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score. To assess the accuracy of the responses in accordance with the medical guidelines, two independent medical professionals rated them on a 3-point Likert scale (0 - incorrect, 1 - partially correct or incomplete, 2 - correct). This allowed us to gauge the compliance of the chatbot responses with the medical guidelines. Results Results: In our study, we posed a total of 45 questions to each of the chatbots. Out of the five chatbots, Microsoft Copilot, PiAI, and ChatGPT were able to respond to all the questions. The length of the responses varied, with PiAI providing the shortest average response length of 7.26 words, and Bard providing the longest at 18.9 words. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT). These results highlight the variability in the readability and complexity of responses generated by different chatbots. Readability analysis is presented in table 1. Conclusion This study indicates that chatbots vary in length, quality and readability. They answer each question in their way, based on data they have pulled from the network. Our data suggests that people who want information from a chatbot need to be careful and verify the answers they get.

Lipid disorders increase the risk for the development of cardiometabolic disorders, including type 2 diabetes, atherosclerosis, and cardiovascular disease. Lipids levels, apart from diet, smoking, obesity, alcohol consumption, and lack of exercise, are also influenced by genetic factors. Recent studies suggested the role of long noncoding RNAs (lncRNAs) in the regulation of lipid formation and metabolism. Despite their lack of protein-coding capacity, lncRNAs are crucial regulators of various physiological and pathological processes since they affect the transcription and epigenetic chromatin remodelling. LncRNAs act as molecular signal, scaffold, decoy, enhancer, and guide molecules. This review summarises available data concerning the impact of lncRNAs on lipid levels and metabolism, as well as impact on cardiovascular disease risk. This relationship is significant because altered lipid metabolism is a well-known risk factor for cardiovascular diseases, and lncRNAs may play a crucial regulatory role. Understanding these mechanisms could pave the way for new therapeutic strategies to mitigate cardiovascular disease risk through targeted modulation of lncRNAs. The identification of dysregulated lncRNAs may pose promising candidates for therapeutic interventions, since strategies enabling the restoration of their levels could offer an effective means to impede disease progression without disrupting normal biological functions. LncRNAs may also serve as valuable biomarker candidates for various pathological states, including cardiovascular disease. However, still much remains unknown about the functions of most lncRNAs, thus extensive studies are necessary elucidate their roles in physiology, development, and disease.

The aim of the study is to evaluate and compare the quality and readability of responses generated by five different artificial intelligence (AI) chatbots—ChatGPT, Bard, Bing, Ernie, and Copilot—to the top searched queries of erectile dysfunction (ED). Google Trends was used to identify ED-related relevant phrases. Each AI chatbot received a specific sequence of 25 frequently searched terms as input. Responses were evaluated using DISCERN, Ensuring Quality Information for Patients (EQIP), and Flesch-Kincaid Grade Level (FKGL) and Reading Ease (FKRE) metrics. The top three most frequently searched phrases were “erectile dysfunction cause”, “how to erectile dysfunction,” and “erectile dysfunction treatment.” Zimbabwe, Zambia, and Ghana exhibited the highest level of interest in ED. None of the AI chatbots achieved the necessary degree of readability. However, Bard exhibited significantly higher FKRE and FKGL ratings (p = 0.001), and Copilot achieved better EQIP and DISCERN ratings than the other chatbots (p = 0.001). Bard exhibited the simplest linguistic framework and posed the least challenge in terms of readability and comprehension, and Copilot’s text quality on ED was superior to the other chatbots. As new chatbots are introduced, their understandability and text quality increase, providing better guidance to patients.

The aim of the article is to highlight the key role of artificial intelligence in modern oncology. The search for scientific publications was carried out through the following web search engines: PubMed, PMC, Web of Science, Scopus, Embase and Ebsco. Artificial intelligence plays a special role in oncology and is considered to be the future of oncology. The largest application of artificial intelligence in oncology is in diagnostics (more than 80%), particularly in radiology and pathology. This can help oncologists not only detect cancer at an early stage but also forecast the possible development of the disease by using predictive models. Artificial intelligence plays a special role in clinical trials. AI makes it possible to accelerate the discovery and development of new drugs, even if not necessarily successfully. This is done by detecting new molecules. Artificial intelligence enables patient recruitment by combining diverse demographic and medical patient data to match the requirements of a given research protocol. This can be done by reducing population heterogeneity, or by prognostic and predictive enrichment. The effectiveness of artificial intelligence in oncology depends on the continuous learning of the system based on large amounts of new data but the development of artificial intelligence also requires the resolution of some ethical and legal issues.

Abstract Background As the population ages, the prevalence of chronic diseases such as cardiovascular disease (CVD) is expected to increase significantly. Non-pharmacological methods are increasingly being sought to manage cardiovascular disease in the presence of other chronic diseases. The Chronic Disease Self-Management Program (CDSMP) originating over at Stanford University is a group-based education and support program that teaches people with chronic diseases how to manage their conditions and improve their quality of life. Purpose Evaluation of the effectiveness of the 6-week CDSMP workshops among individuals with CVD and chronic illnesses as well as the identification of potential areas for improvement and dissemination of the program. Methods The effectiveness of the workshops was assessed by conducting surveys before the workshops and immediately after the end of the 6-week workshops. The following tools were used to assess the effectiveness: visual analogue scale VAS for the level of pain, fatigue and sleep problems, Mini-COPE questionnaire, The Patient Health Questionnaire (PHQ-9) and the Chronic Disease Self-Efficacy Scales developed by the authors of the program. The results are presented as median and interquartile range (IQR). The Wilcoxon signed-rank test was used to analyze the values before and after the workshops. Results A 64-pts with CVD (60% hypertension) a mean age of 70 years. The participants were predominantly female (90%) and male (10%).The study proved the effectiveness of the workshops. All three indicators measured on an analogue scale improved after 6 weeks of workshops. Pain level decreased from 5 (IQR: 2-6) to 3 (IQR:1-5), p=0.022; fatigue level decreased from 6 (IQR: 4-8) to 5 (IQR: 3-6), p=0.052 and sleep problems from 6 (IQR: 2-7) to 3 (IQR: 1-5), p <0.001. In the Mini-COPE questionnaire, the greatest change was noted in the ability of looking for advice and help from others (p=0.009). There was also a statistically significant decrease in the overall PHQ-9 value: before the workshops the median was 7 (IQR: 3-11), and after the workshops 6 (IQR: 3-9), p=0.034. As for the Chronic Disease Self-Efficacy questionnaire, participants achieved the greatest benefits in the domains of: disease management at a general level, coping with symptoms of the disease and coping with difficult emotions and depressive moods. Conclusion The CDSMP can be a valuable tool for managing these complex issues and improving the health and well-being of older adults with CVD and chronic illnesses.

Artery stiffness is a risk factor for cardiovascular disease (CVD). The measurement of pulse wave velocity (PWV) between the carotid artery and the femoral artery (cfPWV) is considered the gold standard in the assessment of arterial stiffness. A relationship between cfPWV and regional PWV has not been established. The aim of this study was to evaluate the influence of gender on arterial stiffness measured centrally and regionally in the geriatric population. The central PWV was assessed by a SphygmoCor XCEL, and the regional PWV was assessed by a new device through the photoplethysmographic measurement of multi-site arterial pulse wave velocity (MPPT). The study group included 118 patients (35 males and 83 females; mean age 77.2 ± 8.1 years). Men were characterized by statistically significantly higher values of cfPWV than women (cfPWV 10.52 m/s vs. 9.36 m/s; p = 0.001). In the measurement of regional PWV values using MPPT, no such relationship was found. Gender groups did not statistically differ in the distribution of atherosclerosis risk factors. cfPWV appears to be more accurate than regional PWV in assessing arterial stiffness in the geriatric population.

COVID-19 is a complex multisystemic disease that can result in long-term complications and, in severe cases, death. This study investigated the effect of COVID-19 on carotid–femoral pulse wave velocity (cfPWV) as a measurement to evaluate its impact on arterial stiffness and might help predict COVID-19-related cardiovascular (CV) complications. PubMed, Web of Science, Embase, and the Cochrane Library were searched for relevant studies, and meta-analysis was performed. The study protocol was registered in PROSPERO (nr. CRD42023434326). The Newcastle–Ottawa Quality Scale was used to evaluate the quality of the included studies. Nine studies reported cfPWV among COVID-19 patients and control groups. The pooled analysis showed that cfPWV in COVID-19 patients was 9.5 ± 3.7, compared to 8.2 ± 2.2 in control groups (MD = 1.32; 95% CI: 0.38–2.26; p = 0.006). A strong association between COVID-19 infection and increased cfPWV suggests a potential link between the virus and increased arterial stiffness. A marked increase in arterial stiffness, a known indicator of CV risk, clearly illustrates the cardiovascular implications of COVID-19 infection. However, further research is required to provide a clearer understanding of the connection between COVID-19 infection, arterial compliance, and subsequent CV events.

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): WHO Reference, 2022/1216167-0, Promoting health and well-being and Flagship initiative- Empowerment through Digital Health. “Older people and COVID-19- new challenges for public health in Poland”. Background The current epidemiological situation related to the COVID-19 pandemic is one of the biggest public health challenges and is associated with many negative phenomena such as the spread of misinformation. Older adults (especially with cardiovascular diseases) with their specific needs, concerns and preferred sources of information are the most vulnerable group in the rapidly evolving world. Objective Identify areas of interest, preferred sources of information related to the COVID-19 pandemic among older adults and verify their e-health competencies. Material and method The study was conducted between February 2022 and July 2022. Both groups of older and younger adults completed a self-administered questionnaire that addressed respondents' interests, questions and sources of information about COVID-19 that they used. Additionally, respondents filled out the PL- eHEALS questionnaire to measure e-health competencies. Results The study included a total of 573 participants (older adults - 303 participants, young adults - 270). The three issues that attracted the largest interest among the elderly were COVID-19 treatment methods (45.5%) COVID-19 symptoms (36.6%) and the impact of COVID-19 on chronic diseases (35%). The main source of information about COVID-19 was television (84.5%). As much as 84% of the elderly have never interacted with a chatbot and only 18% believe it could be a reliable source of information on COVID-19. The e-health competencies of the elderly were significantly lower than the younger generation’s (Fig. 1). Conclusions This study found intergenerational differences in COVID-19 information needs. Older adults need basic information about COVID-19 and prefer using traditional media.

next to the road was calculated and compared to the measurement results.Finally, tendencies