Informatics for health & social care最新文献

Digital service provision became necessary during and after the COVID-19 pandemic highlighting the technological disparity experienced by healthcare professionals and healthcare users. eHealth Literacy skills are mostly measured with the use of the eHeals, but recently more instruments have been developed to meet this need. The aim of the study was to validate and compare the two scales in Greek: the eHeals and the revised eHeals-Extended. In total, 401 participants replied to the eHeals, the revised eHeals-Extended, and the HLS-EU-Q16. The eHeals scales provided good psychometric properties. The validation of the eHeals confirmed the two dimensions with high internal consistency (total score α = .91, eHeals1 α = .88, eHeals2 α = .78). The revised eHeals-Extended exploratory analysis extracted five factors with satisfactory internal consistency (Cronbach's α = .62-.89): awareness and quality of resources online, understanding online information, smart on the net, accessing and validating online information and perceived efficiency. The use of the revised eHeals-Extended and eHeals validated in Greek, could be valuable tools in clinical and research settings. The eHeals could be used as an additional tool when eHealth Literacy is not the core concept measured and the revised eHeals-Extended can be used when researchers wish to measure eHealth Literacy concept more thoroughly.

Patient portals are secure online platforms that offer patients access to various functions such as personal health information. While patient portals are being increasingly offered by health services, there are limited data on their use for persons living with home mechanical ventilation (HMV) and/or long-term tracheostomy. This study, conducted at an Australian hospital's home mechanical ventilation and long-term tracheostomy services, aimed to explore the perspectives and attitudes of patients and carers regarding the introduction of a patient portal. There were 231 survey responses and 6 semi-structured interview participants. Interest in using a patient portal was high with 87% of survey respondents indicating that they would consider using a patient portal if it were offered. Those that were more likely to be interested were younger, had higher levels of education, and reported being confident with using technology and accessing health information. The functions of a patient portal that were of most interest were the ability to view their own health information including ventilation and/or tracheostomy information and the ability to order ventilation and tracheostomy-related equipment. This study is the first step of a user-centered design for the implementation of a patient portal for persons living with home mechanical ventilation and/or long-term tracheostomy.

CarerHelp is an online resource supporting Australian family carers providing palliative care to loved ones at home. To confidently care for a dying person, family carers require access to evidence-based resources, accessible day or night when healthcare professionals or services are unavailable. Given carer diversity, usability evaluations assist online developers in shaping information experiences to family carer's needs and abilities. This study aimed to use a comprehensive usability testing protocol to evaluate the prototype, with a sample involving six family carers aged 36-74 years. A concurrent think-aloud approach generated quantitative and qualitative feedback from scenario-based tasks. Single ease questions (SEQ) and computer satisfaction usability questionnaire (CSUQ) assessed end-user attitudes. Performance, SEQ, and CSUQ were calculated, errors were analyzed, and severity rating applied. Overall, family carers identified 54 errors. All carers detected highly severe errors impacting critical function; technical ability influenced error detection, affecting navigation, utility, and content. Language modification was guided by family carers' narratives, contextualizing interface use with real-life experiences. For the multidisciplinary development team, formal reporting of findings was advantageous in informing meaningful reiterative interface changes and giving a voice to family carers. This approach was essential to assist as many carers as possible in accessing palliative care information when needed.

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 (N = 8) and public housing residents (N = 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.

Introduction: 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.

Methods: This study explores which factors contribute most to the gap between chronological and phenotypic ages. We combined ten machine learning regression techniques applied to the NHANES dataset, containing demographic, laboratory and socioeconomic data from 41,474 patients, to identify the most important features. We then used clustering analysis and a mixed-effects model to stratify by sex, ethnicity, and education.

Results: We identified 28 demographic, biological and environmental factors related to a significant gap between phenotypic and chronological ages. Stratifying for sex, education and ethnicity, we found statistically significant differences in the outcome distributions.

Conclusion: By showing that health risk prevention should consider both biological and sociodemographic factors, we offer a new approach to predict aging rates and potentially improve targeted prevention strategies for age-related conditions.

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.

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.

Objective: 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.

Methodology: We collected data from Kaggle, which included information on age, body mass index, menstrual cycle length, follicle-stimulating hormone, hair growth, and more. Using this data, we trained several traditional ML and ensemble algorithms to predict PCOS.

Results: Among the traditional ML algorithms, Logistic Regression emerged as the best, boasting the highest accuracy of 0.91 and an AUC of 0.90. In ensemble algorithms, the Blending algorithm outperformed other ensemble methods, also achieving an accuracy of 0.91 and an AUC of 0.90, with a balanced precision and recall of 0.88.

Significance of the research: These results establish Logistic Regression and the Blending algorithm as optimal choices for accurate and reliable PCOS prediction, demonstrating strong discriminative power and the ability to correctly classify PCOS cases.

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.

This study empirically evaluates the functionality coverage of 18 mobile applications (apps) for Postnatal care including a recently developed app in Morocco "Mamma&Baby". 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 "Mamma&Baby" app, efforts are required to boost the number of downloads, optimize its visibility, and attract the highest number of users.