Journal of Medical Internet Research

eHealth and mobile health (mHealth) interventions are promising in promoting healthy behaviors among children and adolescents.

Functional neurological disorder (FND) is one of the commonest conditions in neurological practice, describing symptoms like paralysis and seizures that can be severe and disabling. It is a diagnosis that is confirmed clinically rather than by scans or laboratory results. It is a stigmatized and widely misperceived condition, and since the emergence of long COVID, there has been some conflation of FND with other conditions, which has caused further misunderstanding. Social media has become increasingly popular for patients to learn and interact about their conditions, and the information that they seek and receive may be shaped by many factors. Prior to this study, the online discourse about FND had not been described in the literature.

First responders, military personnel, and veterans face disproportionate risk for mental health and wellness issues. Stigma and confidentiality are common barriers to traditional services. Mobile health interventions offer anonymous, convenient, and cost-effective alternatives.

Enhanced Recovery After Surgery (ERAS) guidelines aim to optimise perioperative care and improve recovery outcomes. The guidelines contain clinician- and patient-led recommendations for pre-and post-operative care, with patient-led recommendations including smoking cessation, early mobilisation and early resumption of eating and drinking. While adherence to these recommendations can improve recovery outcomes, it’s typically low, and many patients require support. Digital Health Interventions (DHIs) are increasingly accepted as useful tools in delivering individualised healthcare,and have the potential to support adherence to ERAS guidelines. Evidence suggests intervention use is optimised when DHIs are considered acceptable to end-users. RecoverEsupport is a DHI designed to support patient adherence to surgical recovery guidelines, following breast cancer surgery, intended as part of a blended approach with standard care.

Mood monitoring and Ecological Momentary Assessment (EMA) hold promise for supporting self-management and data collection in Bipolar Disorder (BD), but the effectiveness of these depends crucially on the preferences and perspectives of those who use them. To date, these user experiences have not been systematically synthesised.

Artificial intelligence-assisted conversational agents have been applied and developed in outpatient departments to improve health services in China. However, there has been little research that evaluates the effect of artificial intelligence-assisted conversational agents on the patient experience related to physicians during outpatient visits.

In Germany, the messaging app Telegram served as a tool to organize protests against public health measures during the COVID-19 pandemic. A community of diverse groups formed around these protests, which used Telegram to discuss and share views outside of the general public discourse and mainstream information ecosystem. This increasingly included conspiracy theories and extremist content, propagated by sources that opposed the mainstream positions of the government and traditional media. While the use of such sources has been thoroughly investigated, the role of mainstream information in these communities remains largely unclear.

Artificial intelligence (AI) chatbots, driven by advances in natural language processing (NLP), can analyze and generate human language through computational linguistics and machine learning. Despite the rapid development of large language models, little investigation has been conducted to assess whether AI chatbot-delivered educational conversations can achieve a similar level of efficacy as human-delivered conversations.

Nonsuicidal self-injury (NSSI) is a critical public health concern among university students, often considered a gateway behavior to suicide. With the widespread use of mobile phones, understanding the association between specific mobile phone use behaviors (eg, presleep and postwake mobile phone use) and NSSI has become increasingly important for targeted prevention.

Patient messaging technologies offer treatment information and recommendations through web-based platforms, patient portals, mobile apps, and SMS text messaging. Many of these technologies have started to incorporate messages that are crafted by artificial intelligence (AI). Such tools are most effective when constructed with theoretical grounding and iterative input from end users. Thus, we outline a human-centered design approach for developing patient messaging content that aligns with self-determination theory (SDT), a widely used framework that has shown positive impacts on health behavior change. We illustrate our approach step-by-step for the development of messages that promote evidence-based treatment opportunities for patients with chronic pain. Messages were initially developed by subject matter experts and refined using SDT constructs (autonomy, competence, and relatedness) and motivation and behavior change techniques. Using a rapid prototyping approach, we sequentially met with 3 patient engagement boards to elicit feedback on message prototypes and enhance their content. We synthesized and aligned disparate feedback across boards with SDT and motivation and behavior change techniques. Drawing upon the input from the engagement boards, existing co-design approaches, and the field of human-centered AI, we recommend strategies to collaborate with patient partners to enhance the readability and clarity of messaging content. Recommended strategies include (1) involve engagement boards early in messaging framing and modality selection, (2) represent diverse perspectives when refining messages, (3) acknowledge and set expectations to integrate unique experiences and views, (4) prioritize message tailoring for the population of interest, (5) incorporate continual feedback mechanisms, and (6) keep the human interaction in patient-facing messages. By illuminating the process of developing message content that aligns with SDT constructs and providing guidance for iterative patient engagement and practical prototyping, we hope this tutorial can be used to enhance patient messaging content and improve uptake of evidence-based treatments. Our approach and recommendations can also guide multidisciplinary research and design teams to build patient-centered health messages. This tutorial has special consideration for future AI-guided messaging interventions, as patients are typically not involved in message content development or framing, but early engagement can potentially mitigate known AI concerns related to privacy, transparency, and fairness. As technologies and patient populations change over time, linking continual end user input with theoretical grounding plays a key role in simplifying complex medical information and promoting understanding of treatment opportunities that can ultimately improve health outcomes.

Dynamic predictive modeling using electronic health record data has gained significant attention in recent years. The reliability and trustworthiness of such models depend heavily on the quality of the underlying data, which is, in part, determined by the stages preceding the model development: data extraction from electronic health record systems and data preparation. In this paper, we identified over 40 challenges encountered during these stages and provided actionable recommendations for addressing them. These challenges are organized into 4 categories: cohort definition, outcome definition, feature engineering, and data cleaning. This comprehensive list serves as a practical guide for data extraction engineers and researchers, promoting best practices and improving the quality and real-world applicability of dynamic prediction models in clinical settings.

Emerging challenges, such as climate change and noncommunicable diseases, threaten the “survive and thrive” agenda for children and adolescents. These challenges have added to the existing burden of newborn and child morbidity and mortality. Digital solutions hold promising potential to address children’s evolving health needs, especially in reaching remote areas, increasing inclusion, and ensuring equitable primary health care. This commentary raises the question, are we ready to use digital solutions and artificial intelligence to achieve transformations in child health in South Asia? If not, what is the paradigm shift required to design and implement digital and artificial intelligence solutions at-scale that are effective, sustainable, and beyond small pilots?