Recent Articles
Effective pain management is a cornerstone of cancer palliative care, yet it remains challenging in low- and middle-income countries (LMICs) due to limited resources, regulatory constraints, and a lack of objective tools. While wearable technologies offer promise for augmenting pain-related patient-reported outcomes (PROs) with physiological data, their usability in LMIC palliative settings is underexplored.
Maternal evaluation during routine antenatal care visits may reduce maternal morbidity and mortality by identifying and addressing issues early on. A health recommender system could help health professionals and pregnant women monitor daily health parameters, provide tailored recommendations, and support timely antenatal care.
Human papillomavirus (HPV) vaccination is a proven and effective tool for preventing several types of cancers, yet vaccination rates among young adults remain suboptimal, particularly among those not enrolled in 4-year colleges. This population can be more difficult to reach due to fewer established institutional touchpoints, limited engagement with campus-based health services, and greater variability in access to preventive care. At the same time, social media has become a dominant source of information for young adults, with TikTok (ByteDance) emerging as one of the most widely used platforms. Approximately 41% of TikTok’s users are between the ages of 16 and 24 years, making it a potentially important channel for public health communication. However, little is known about how noncollege young adults perceive HPV-related content on TikTok, or how influencers themselves view their role in communicating about vaccination.
Early detection in primary care could improve pancreatic cancer survival, but diagnosis is often delayed due to the low prevalence of the disease, the nonspecific nature of early symptoms, and the broad range of conditions and volume of consultations managed by general practitioners (GPs). In Australia, improving pancreatic cancer outcomes, including via earlier diagnosis, is a priority being progressed under the National Pancreatic Cancer Roadmap developed by Cancer Australia. Computerized clinical decision support systems (CDSSs) have shown promise in aiding timely cancer diagnosis; however, barriers to adopting CDSS such as mistrust of the recommendations or not being embedded in the clinical workflow remain. Simulation techniques, which offer flexible and cost-effective ways to evaluate digital health interventions, can be used to test CDSS before real-world implementation.
Registered Dietitian Nutritionists (RDNs) - referred to as registered dietitians (RDs) in Japan - contribute to disease management, prevention of complications, and improvement of quality of life (QOL) through individualized nutritional guidance. However, these techniques often rely on individual experience, leading to variations in quality. The Nutrition Care Process (NCP) provides a standardized framework for nutritional care, but the specific techniques used in clinical practice and their interrelationships remain unclear. Interpretive Structural Modeling (ISM) is a method that visualizes and hierarchically organizes interrelationships among multiple elements, making it useful for structuring complex practical skills. Clarifying the structure of nutritional guidance techniques may therefore support the standardization of practice and the development of educational frameworks.
Hispanic youth in the United States have the highest rates of pediatric obesity and do not often meet national guidelines for physical activity and dietary intake. Family-based interventions can improve health outcomes in both youth and their parents and are highly relevant to Hispanics due to the cultural value of familismo (familism). However, few existing family-based obesity prevention interventions for Hispanics target adolescents and their parents, and those that do are not designed to facilitate widespread reach.
The ongoing opioid epidemic has been associated with increases in emergency department visits and hospitalizations for drug overdose and injection-related infections. These encounters with the healthcare system provide an opportunity to offer drug treatment linkage and support for people with opioid use disorder (PWOUD). There is a need for interventions that enhance linkage to and engagement in treatment with medications for OUD (MOUD) for PWOUD identified in hospital settings as they transition back to community settings.
To elucidate the complex relationship between spirituality and obsessive-compulsive disorder (OCD), we performed a qualitative analysis of messages (n=225) referencing spiritualities in r/OCD, a public online peer support forum for people with OCD with over 250,000 users. Two central themes emerged: (1) Influence of spirituality on OCD symptom manifestation and (2) impact of OCD on relationship with spirituality. Some users reported scrupulosity and/or disillusionment with their spirituality, while others reported remaining persistently faithful, which they sometimes attributed their recovery from OCD to.
In the UK, there was an increased demand for young people’s mental health helpline services during COVID-19, when face-to-face services were often inaccessible. Despite this, there is scant research examining young people’s experiences with these helplines during the pandemic and post-pandemic periods.
The integration of artificial intelligence (AI) into clinical decision support systems (CDSSs) for mechanical ventilation in intensive care units (ICUs) holds great potential. However, the lack of transparency and explainability hinders the adoption of opaque AI models in clinical practice. Explanation user interfaces (XUIs), incorporating explainable AI algorithms, are considered a key solution to enhance trust and usability. Despite growing research on explainable AI in health care, little is known about how clinicians perceive and interact with such explanation interfaces in high-stakes environments such as the ICU. Addressing this gap is essential to ensure that AI-supported CDSS are not only accurate but also trusted, interpretable, and seamlessly integrated into clinical workflows.
Poor sleep is a concerning public health problem in the United States. Previous sleep interventions often face barriers such as high costs, limited accessibility, and low user engagement. Recent advancements in artificial intelligence (AI) technologies offer a novel approach to overcoming these limitations. In response, our team developed a prototype AI sleep chatbot powered by a large language model to deliver personalized, accessible sleep support.
