The world's population is aging rapidly, leading to increased public health and economic burdens due to age-related cardiovascular and neurodegenerative diseases. Early risk detection is essential for prevention and to improve the quality of life in elderly individuals. Plus, health risks associated with aging are not directly tied to chronological age, but are also influenced by a combination of environmental exposures. Past research has introduced the concept of "Phenotypic Age," which combines age with biomarkers to estimate an individual's health risk.

Infodemic is closely related to all healthcare professionals. Nurses take the lead in health professions that spend a long time with the society and try to present accurate information due to their roles. Infodemic is the massive spread of false or unreliable information in times of crisis. This study aims to develop a valid and reliable tool that identifies individuals' approaches to infodemic. This study was methodological. The sample consisted of 504 people ( = 504). Data were collected using the Information Form and the Infodemic Scale. The sample was divided into two for the explanatory factor analysis (EFA) and the confirmatory factor analysis (CFA). The scale was developed using the scale development process in health and social research, which consists of three steps. The EFA revealed 14 items under two factors, which are awareness and literacy. Item factor loadings were found to range between 0.334 and 0.907. The CFA confirmed the two-factor scale structure. The total Cronbach's alpha of the scale was 0.87, and the composite reliability value was 0.89. The intra-class correlation coefficient (ICC) value was found to be 0.857. According to psychometric evaluations, the Infodemic Scale is a valid and reliable instrument that can make consistent measurements.

The socioeconomic costs of neurodegenerative diseases (NDs) are highly affected by comorbidities. This study aims to enhance our understanding of the prevalent complications of NDs through the lens of network analysis. A multimorbidity network (MN) was constructed based on a longitudinal EHR dataset of 93,647,498 diagnoses of 824,847 patients. The association between the conditions was measured by two metrics, i.e. Phi-correlation and Cosine Index (CI). Based on multiple network centrality measures, a fused ranking list of the prevalent multimorbidities was provided. Finally, class-level networks depicting the prevalence and strength of diseases in different classes were constructed. The general MN included 928 diseases and 337,253 associations. Considering a 99% confidence level, two networks of 575 relationships were constructed based on Phi-correlations (73 diseases) and CI (102 diseases). Five out of 19 ICD-9 categories did not appear in either of the networks. Also, ND's immediate MNs for the top 50% of the significant associations included 42 relationships, whereas the Phi-correlation and CI networks included 36 and 34 diseases, respectively. Thirteen diseases were identified as the most notable multimorbidities based on various centrality measures. The analysis framework helps practitioners toward better resource allocations, more effective preventive screenings, and improved quality of life for ND patients and caregivers.

The prevalence of dementia is increasing due to the aging population, leading to significant investments in information technology-based dementia care solutions such as mobile dementia prevention services (MDPS). These services aim to facilitate early diagnosis and prevent cognitive decline, with the ultimate goal of reducing medical costs. This study examines how aging individuals perceive and evaluate MDPS and how these perceptions influence their adoption intentions. Drawing from a coping perspective, we analyze the impact of susceptibility and severity dimensions in dementia risk perception on the perceived usefulness and self-incongruence in MDPS adoption. Focusing on middle-aged and older adults aged 55 and above, the study reveals significant but contrasting effects of susceptibility and severity. Perceived susceptibility of dementia is associated with emotion-focused coping, positively influencing self-incongruence with MDPS. Perceived severity of dementia is linked to problem-focused coping, negatively impacting self-incongruence but positively influencing the perceived usefulness of MDPS, promoting adoption. These findings provide insights into promoting MDPS by considering dementia risk perceptions and contribute to the development of effective dementia-related strategies for aging individuals.

Computerized training platforms could be an accessible means for older adults to maintain cognitive health, and several such tools are already commercially available. However, it remains unclear whether older adults use these tools if training is not externally prescribed. We explored older adults' self-initiated experiences with cognitive training. We conducted semi-structured interviews with 13 community-dwelling adults aged 58-85 years, comprising university retirees ( = 8) and public housing residents ( = 5). Interviews were analyzed by thematic analysis. No participants voluntarily used cognitive training, and those who had done so previously reported negative experiences. Several factors shaped older adults' engagement with cognitive training, especially a preference for stimulating activities that are organic and inherently enjoyable. We reveal a mismatch between older adults' priorities and the interventions currently available and uncover issues of access and interest among low-income and minority individuals. We suggest ways to better align future interventions with older adults' priorities.

Health-seeking behavior represents the actions taken to prevent the disease and promote health. It emphasizes both the illness response and the healthcare utilization driven by perceived threat and effectiveness of the preventive behavior. This study aims to scrutinize the progression of research conducted on health-seeking behavior in high-risk period such as COVID-19 using bibliometric analysis. The bibliometric analysis is performed on Scopus and Web of Science databases. Research articles in the English language were extracted using keywords, such as health-seeking behavior and COVID. Eight hundred twenty-five research articles at the final and early publication stage in the English language were extracted from Scopus and 623 from WoS using the keywords Health Seeking Behavior and COVID. Of these, 259 in Scopus and 109 in WoS were selected for the final study following the authors' eligibility criteria. It analyses the research directions, countries of publications, core journals, leading authors and institutions and important publications followed by research trends in this field. It summarizes the academic interest of the researchers in health-seeking behavior in low- and middle-income countries. The paper informs and directs researchers and policymakers on the state of research in health-seeking behavior during high-health risk periods.

In this study, we aimed to develop a machine learning (ML) model for predicting Polycystic Ovary Syndrome (PCOS) based on demographic, clinical, and biochemical parameters.

This study empirically evaluates the functionality coverage of 18 mobile applications (apps) for Postnatal care including a recently developed app in Morocco ". This evaluation is based on a comparison of the COSMIC _ISO 19,761 functional size of these apps with the score obtained in a previous evaluation based on functions extraction through a quality assessment questionnaire. This comparison allows to discuss the relationship between the functional size of the 18 apps, their users' ratings in the Play Store as well as the number of downloads. While for most of the assessed apps, there is only a small shift between the rankings of the two evaluations, for some apps, the shift is huge due to the number of features added and not covered by the score previously obtained. This study illustrates the use of COSMIC as an effective method for corrective or evolutionary updates since it takes into account all the functions and features of postnatal apps. For the " app, efforts are required to boost the number of downloads, optimize its visibility, and attract the highest number of users.

In Indonesia, the number of People Living with Dementia (PLWD) is predicted to be rising continuously. PLWD need help operating cell phones for their daily needs. Numerous mobile health applications have been innovated to deliver better dementia care. The objective of this study is to identify the technological acceptance and features needed by PLWD and caregivers in Indonesia. This study started with questionnaire development through focused-group discussion with experts, caregivers and PLWD. It was followed by item development involving experts in geriatrics (psychiatry, internal medicine, medical rehabilitation and neurology) to prepare an online questionnaire. We considered using jargon and words that are familiar to users. The data collected was analyzed for the technological acceptance model (TAM) using Structural Equation Model (SEM). This study showed that perceived usefulness and ease of use of the technology have positively correlated to the actual use. This study reflects the need for social connectedness and information for PLWD. For caregivers, the main feature needed is information related to dementia symptoms and treatment. The understanding gained from this study can be used to improve strategies related to developing mobile health technology for PLWD and caregivers.

Poor adherence to tuberculosis (TB) treatment leads to further disease transmission, worsened outcomes, and the development of drug resistance. Digital adherence technologies may facilitate a more patient-centered approach for improving TB treatment outcomes than current strategies. The objective of this study was to evaluate and explore improving usability of the TB Treatment Support Tools (TB-TST) mobile application. We used an iterative convergent mixed-method design consisting of two quantitative surveys and a qualitative think-aloud interview. Testing was conducted in three testing cycles consisting of a total of 16 interviews and 26 surveys. Results were thematically analyzed and reported to the development team during weekly team meetings. Participants rated the TB-TSTs application as having high usability and the iterative approach resulted in several refinements to the application in response to participant feedback. These refinements were well received during qualitative interviews but did not result in a statistically significant improvement in usability testing scores between cycles. Using an iterative convergent mixed-method design was an effective method for refining our mHealth application. Data collected from think-aloud interviews, the MAUQ, and the Health-ITUES identified key areas of application design that needed refinement.

To assess the overall experience of a patient in a hospital, many factors must be analyzed; nonetheless, one of the key aspects is the performance of nurses as they closely interact with patients on many occasions. Nurses carry out many tasks that could be assessed to understand the patient's satisfaction and consequently, the effectiveness of the offered services. To assess their performance, traditionally, expensive, and time-consuming methods such as questionnaires and interviews have been used; nevertheless, the development of social networks has allowed the patients to convey their opinions in a free and public manner. For that reason, in this study, a comprehensive analysis has been performed based on patients' opinions collected from a feedback platform for health and care services, to discover the topics about nurses the patients are more interested in. To do so, a topic modeling technique has been proposed. After this, sentiment analysis has been applied to classify the topics as satisfactory or unsatisfactory. Finally, the results have been compared with what the patients think about doctors. The results highlight what topics are most relevant to assess the patient satisfaction and to what extent. The results remark that the opinion about nurses is, in general, more positive than about doctors.

Pushes toward earlier detection of Alzheimer's disease (AD)-related cognitive changes are creating interest in leveraging technologies, like cellphones, that are already widespread and well-equipped for data collection to facilitate digital monitoring for AD. Studies are ongoing to identify and validate potential "digital biomarkers" that might indicate someone has or is at risk of developing AD dementia. Digital biomarkers for AD have potential as a tool in aiding more timely diagnosis, though more robust research is needed to support their validity and utility. While there are grounds for optimism, leveraging digital monitoring and informatics for cognitive changes also poses ethical challenges, related to topics such as algorithmic bias, consent, and data privacy and security. As we confront the modern era of Alzheimer's disease, individuals, companies, regulators and policymakers alike must prepare for a future in which our day-to-day interactions with technology in our daily life may identify AD-related cognitive changes.

Underdiagnosis, misdiagnosis, and patterns of social inequality that translate into unequal access to health systems all pose barriers to identifying and recruiting diverse and representative populations into research on Alzheimer's disease and Alzheimer's disease related dementias. In response, some have turned to algorithms to identify patients living with dementia using information that is associated with this condition but that is not as specific as a diagnosis. This paper explains six ethical issues associated with the use of such algorithms including the generation of new, sensitive, identifiable medical information for research purposes without participant consent, issues of justice and equity, risk, and ethical communication. It concludes with a discussion of strategies for addressing these issues and prompting valuable research.

Telehealth transforms the healthcare system and provides the population with equal access to healthcare services at distance. This study aimed to investigate nursing students' perceptions toward telenursing. Students' knowledge, attitudes, advantages, disadvantages, barriers, and factors that affect the intention toward telenursing implementation were addressed. This study was conducted using a descriptive design. The study participants were 313 undergraduate nursing students. Data were gathered through a web-based survey from June to August 2022 and analyzed using SPSS version 22. Fifty-four-point-six percent (54.6%) of the students were male and 45.4% were female. Around one-fourth were internship students. Most students had access to the internet 97.4%. The results revealed that nursing students have positive perceptions toward telenursing. Their overall competencies in terms of knowledge, proficiency, awareness, and familiarity were moderate. Students raised some disadvantages and difficulties regarding telenursing; meanwhile, more advantages and suggestions to overcome the disadvantages were reported. It is of utmost importance that nursing education integrates telenursing content and practice to prepare future nurses for the successful implementation of telenursing. More research is still needed to examine the impact of telenursing on nursing practice. Nursing administrators must develop appropriate and prompt interventions to respond to the dramatically changing healthcare environment.

The COVID-19 pandemic has exposed significant gaps in healthcare access, quality, and the urgent need for enhancing the capacity of digital health human resources, particularly in Latin America. During the pandemic, online courses and telehealth initiatives supported by governmental agencies, the Pan American Health Organization, and other public and private resources, have played a crucial role in meeting training demands. This article discusses the role of capacity building programs in digital health within the context of Latin America, with a specific focus on the Peruvian case. We highlight the development of digital health competencies and related policies, while also describing selected experiences related to capacity building in this field. Additionally, we discuss the pivotal role of collaborative partnerships among institutions and countries, emphasizing the importance of culturally relevant training programs in digital health. These initiatives have the potential to accelerate training and research opportunities in Latin America, drawing on the involvement of government agencies, non-governmental organizations, industry, universities, professional societies, and communities.

Google Trends data can be a valuable source of information for health-related issues such as predicting infectious disease trends.

There is a growing literature on the role of mobile health applications (mHealth apps) in supporting older adults and the self-management of personal health. The purpose of this pilot study is to assess the usability of a government-funded mobile health app amongst older Australians and to evaluate whether cognitive function and demographic characteristics (i.e. age, gender, education) are associated with usability. A total of 28 older adults living in a regional city in Australia took part in the study. The participants were recruited using purposive sampling. Data collection instruments consisted of validated cognitive tests, task-based usability tests, and a questionnaire. The data was analyzed using non-parametric strategies. The findings of this study demonstrated that a government-funded, mHealth app was usable by older adults. Users were able to perform basic tasks in an effective and efficient manner. The hypothesis that elderly age would be significantly associated with performance on cognitive tests, as well as usability, was not supported. Performance on some cognitive tests was significantly and positively related to usability. Education and gender were not related to usability. The results suggest that traditional stereotypes surrounding aging and cognitive decline need to be reexamined.

Information visualization (InfoViz) tools offer a potential solution to pain communication challenges. Incongruencies in communication styles between patients with limited English proficiency (LEP), interpreters, and providers contribute to significant disparities in pain care and outcomes. This study's purpose is to evaluate and refine a culturally appropriate InfoViz pain quality assessment tool for LEP Hmong patients. We conducted a three-part iterative user-centered study with LEP Hmong, bilingual Hmong, and Hmong interpreters with (1) participatory design sessions to evaluate and refine pain infographics for inclusion on the tool, (2) card-sorting to organize the infographics to match the mental models of LEP patients, and (3) a tool assessment to identify which tool accurately represented LEP patients' mental models and was preferred in clinical settings. Fifty-five participants provided three common themes for pain infographics refinement: culturally-relevant colors, infographics resembling human anatomy, and action-specific squiggle lines. The card-sorting sessions revealed three organizational themes: sensation ( = 15; 71.4%), localization ( = 6; 28.6%), and severity of pain quality ( = 5; 24.3%). Most participants selected the localization as the most accurate tool and preferred it in clinical settings. Using a multi-step, user-centered approach resulted in a culturally appropriate pain InfoViz tool for LEP Hmong patients.

Childhood asthma is a common and serious chronic lung disease. Mobile health (mHealth) technologies may assist clinical providers, caregivers, and children in managing pediatric asthma. This study evaluated the Nemours app, an mHealth application. We examined: a) frequency of data access by providers and feature use by caregivers (parents/legal guardians) of 5-11-year-old children diagnosed with asthma and b) whether utilization was related to benefits. Nine providers (allergists/pulmonologists) and 80 patient-families (caregiver/child dyads) participated. Two-years of retrospective data were obtained for asthma control, in-person urgent healthcare utilization, and app utilization. Six-months of prospective data included asthma control, in-person urgent healthcare utilization, app utilization, surveys, and health literacy screeners. Providers (56%) accessed app data and caregivers (61%) utilized the app. Caregiver use of messaging feature predicted gains in health literacy scores ( = .44,  = .041), suggesting app use may offer some educational benefits. Implementation of strategies that support app engagement and utilization may help to maximize intended benefits.

While technologies for aging in place are promoted to support care partners and people living with dementia, perspectives of people living with dementia are underrepresented in both use decisions among families and discussions within academia and industry. This mixed-methods study examined the use preferences of twenty-nine people living with mild Alzheimer's disease (AD) for four categories of technologies: location tracking, in-home sensors, web-cameras, and virtual companion robots. Participants completed a novel dyadic intervention, Let's Talk Tech, where they documented their preferences of the four technology categories for care planning purposes. Post-test interviews were thematically analyzed and provide insight into selection processes. Technology preferences varied considerably by and within participant living with mild AD. Excepting location tracking, non-technology and low-technology options were more desirable than the featured technologies. Control over technology use was of great importance to people living with AD. Considerations given to technology preference selection imperfectly fit within the new Health Technology Acceptance Model (H-TAM) developed for older adults. These findings underscore the importance of including people living with dementia in decision making about technologies to support care at home and the need for further personalization and tailorable technological devices to accommodate and align with their preferences.

Medication errors are the third leading cause of death. There are several methods to prevent prescription errors, one of which is to use a Computerized Physician Order Entry system (CPOE). In a CPOE system, necessary data needs to be collected so that making decisions about prescribing medications and treatment plans could be made. Although many CPOE systems have been developed worldwide, studies have yet to identify the necessary data and data elements of CPOE systems. This study aims to identify data elements of CPOE and standardize these data with Fast Healthcare Interoperability Resources (FHIR) to facilitate data sharing and integration with the electronic health record (EHR) system and reduce data diversity.