Journal of Medical Internet Research

Despite extensive research into technology users’ privacy concerns, a critical gap remains in understanding why individuals adopt different standards for data protection across contexts. The rise of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR), and big data has created rapidly evolving and complex privacy landscapes. However, privacy is often treated as a static construct, failing to reflect the fluid, context-dependent nature of user concerns. This oversimplification has led to fragmented research, inconsistent findings, and limited capacity to address the nuanced challenges posed by these technologies. Understanding these dynamics is especially crucial in fields such as digital health and informatics, where sensitive data and user trust are central to adoption and ethical innovation.

The increasing prevalence of mental health issues among adolescents and young adults, coupled with barriers to accessing traditional therapy, has led to growing interest in artificial intelligence (AI)-driven conversational agents (CAs) as a novel digital mental health intervention. Despite accumulating evidence suggesting the effectiveness of AI-driven CAs for mental health, there is still limited evidence on their effectiveness for different mental health conditions in adolescents and young adults.

Chronic pain is a highly prevalent condition, estimated to affect as many as 30% of people worldwide. The need for more innovative solutions for chronic pain management is clear, and digital health technology (DHT) may be the best way to address this challenge. Much of the digital health research focusing on chronic pain focuses on patient-facing solutions; however, DHT for health care professionals (HCPs) is equally important to support evidence-based practice, which, in turn, improves patient outcomes. Despite this, no review has investigated the availability of professional-facing DHT for chronic pain management.

Inhaled medication is the preferred route of administration for patients with chronic obstructive pulmonary disease (COPD). The compliance rate of inhaled medication in patients with COPD is <50%, which increases the risk of acute exacerbations. Considering the complex steps of inhaled medication, improving inhaled medication compliance not only requires consistent medication frequency and medical advice but also an evaluation of whether the patient has mastered the inhaler technique to achieve the correct dose.

Generative large language models (LLMs), such as ChatGPT, have significant potential for qualitative data analysis. This paper aims to provide an early insight into how LLMs can enhance the efficiency of text coding and qualitative analysis, and evaluate their reliability. Using a dataset of semistructured interviews with blind gamers, this study provides a step-by-step tutorial on applying ChatGPT 4-Turbo to the grounded theory approach. The performance of ChatGPT 4-Turbo is evaluated by comparing its coding results with manual coding results assisted by qualitative analysis software. The results revealed that ChatGPT 4-Turbo and manual coding methods exhibited reliability in many aspects. The application of ChatGPT 4-Turbo in grounded theory enhanced the efficiency and diversity of coding and updated the overall grounded theory process. Compared with manual coding, ChatGPT showed shortcomings in depth, context, connections, and coding organization. Limitations and recommendations for applying artificial intelligence in qualitative research were also discussed.

Online health information is widely available, but a substantial portion of it is inaccurate or misleading, including exaggerated, incomplete, or unverified claims. Such misinformation can significantly influence public health decisions and pose serious challenges to health care systems. With advances in artificial intelligence and natural language processing, pretrained large language models (LLMs) have shown promise in identifying and distinguishing misleading health information, although their effectiveness in this area remains underexplored.

The opioid epidemic in the United States remains a major public health concern, with opioid-related deaths increasing more than 8-fold since 1999. Chronic pain, affecting 1 in 5 US adults, is a key contributor to opioid use and misuse. While previous research has explored clinical and behavioral predictors of opioid risk, less attention has been given to large-scale linguistic patterns in public discussions of pain. Social media platforms such as X (formerly Twitter) offer real-time, population-level insights into how individuals express pain, distress, and coping strategies. Understanding these linguistic markers matters because they can reveal underlying psychological states, perceptions of health care access, and community-level opioid risk factors, offering new opportunities for early detection and targeted public health response.

Acute respiratory distress syndrome (ARDS) is a critical condition commonly encountered in the intensive care unit (ICU), characterized by a high incidence and substantial mortality rate. Early detection and accurate prediction of ARDS can significantly improve patient outcomes. While machine learning (ML) models are increasingly being used for ARDS prediction, there is a lack of consensus on the most effective model or methodology. This study is the first to systematically evaluate the performance of ARDS prediction models based on multiple quantitative data sources. We compare the effectiveness of ML models via a meta-analysis, revealing factors affecting performance and suggesting strategies to enhance generalization and prediction accuracy.

Perioperative adverse events (PAEs) pose a substantial global health burden, contributing to elevated morbidity, mortality, and health care expenditures. The adoption of clinical decision support systems (CDSS), particularly mobile-based solutions, offers a promising avenue to address these challenges. However, successful implementation hinges on understanding anesthesia providers’ knowledge, attitudes, and willingness to embrace such technologies.

Escalating mental health demand exceeds existing clinical capacity, necessitating scalable digital solutions. However, engagement remains challenging. Conversational agents can enhance engagement by making digital programs more interactive and personalized, but they have not been widely adopted. This study evaluated a digital program for anxiety in comparison to external comparators. The program used an artificial intelligence (AI)–driven conversational agent to deliver clinician-written content via machine learning, with clinician oversight and user support.

As the optimal treatment for end-stage renal disease, kidney transplantation has proven instrumental in enhancing patient survival and quality of life. Suboptimal medication adherence is recognized as an independent risk factor for poor prognosis, graft rejection, and graft loss. In recent years, the advancement of IT has facilitated the integration of eHealth technologies into medical medication management, offering potential solutions to improve patient adherence. However, their efficacy in kidney transplant recipients remains inconclusive.

It is important to explain early diagnosis and treatment plans to patients of prostate cancer due to the different stages that diagnosis is made at and the corresponding stage-specific treatment options, as well as the varying prognoses depending on the choices made. Although various studies have implemented metaverse-based interventions across diverse clinical settings for medical education, there is a lack of publications addressing the implementation and validation of patient education using this technology.