Journal of Medical Internet Research

With the rapid development of digital biomarkers in Parkinson disease (PD) research, it has become increasingly important to explore the current research trends and key areas of focus.

Childhood and adolescent obesity is a growing global health issue linked to noncommunicable diseases such as cardiovascular disease and type 2 diabetes. Digital health technologies, including mobile apps and web-based programs, offer scalable tools to improve health behaviors, but their effectiveness in young populations remains unclear.

Social media platforms have witnessed a substantial increase in mental health–related discussions, with particular attention focused on attention-deficit/hyperactivity disorder (ADHD) and autism. This heightened interest coincides with growing neurodiversity advocacy. The impact of these changes in the conceptualization of ADHD and autism, and the relationship between the 2 conditions, remains underexplored.

Large language models (LLMs), such as OpenAI’s GPT-3.5, GPT-4, and GPT-4o, have garnered early and significant enthusiasm for their potential applications within mental health, ranging from documentation support to chat-bot therapy. Understanding the accuracy and reliability of the psychiatric “knowledge” stored within the parameters of these models and developing measures of confidence in their responses (ie, the likelihood that an LLM response is accurate) are crucial for the safe and effective integration of these tools into mental health settings.

Acute respiratory distress syndrome (ARDS) is a life-threatening condition associated with high mortality rates. Despite advancements in critical care, reliable early prediction methods for ARDS-related mortality remain elusive. Accurate risk assessment is crucial for timely intervention and improved patient outcomes. Machine learning (ML) techniques have emerged as promising tools for mortality prediction in patients with ARDS, leveraging complex clinical datasets to identify key prognostic factors. However, the efficacy of ML-based models remains uncertain. This systematic review aims to assess the value of ML models in the early prediction of ARDS mortality risk and to provide evidence supporting the development of simplified, clinically applicable ML-based scoring tools for prognosis.

Smoking is a leading cause of preventable death, and people with HIV have higher smoking rates and are more likely to experience smoking-related health issues. The Sense2Quit study introduces innovative advancements in smoking cessation technology by developing a comprehensive mobile app that integrates with smartwatches to provide real-time interventions for people with HIV attempting to quit smoking.

The rapid advancement of Internet of Things and artificial intelligence technologies has driven significant growth in the demand for smart home products, with household penetration projected to increase from 77.6% in 2025 to 92.5% in 2029. Despite this growth, much of the existing research adopts a technology-push approach, focusing primarily on user adoption and acceptance from the perspective of technology providers rather than addressing the evolving needs and experiences of users.

Prognostic models in medicine have garnered significant attention, with established guidelines governing their development. However, there remains a lack of clarity regarding the appropriate circumstances for a) creating and b) implementing tools based on models with limited performance. This commentary addresses this gap by analyzing the pros and cons of tool development and providing a structured outline that includes critical questions to consider in the decision-making process, based on an example for patients with osteoarthritis. We propose three general justifications for the implementation of survey-based models: (1) mitigating expectation bias among patients and clinicians, (2) advancing personalized medicine, and (3) enhancing existing predictive information sources. Nevertheless, it is crucial to acknowledge that implementing such models is always context-dependent and may harm certain patients, necessitating careful consideration of the withdrawal of tool development and implementation in specific cases. To facilitate the identification of these scenarios, we delineate 16 possibilities following the implementation of a personalized prognostic model and compare the consequences to a current one-size-fits-all treatment recommendation on a population level. Our analysis encompasses the possible patient benefits and harms resulting from (not) implementing personalized prognostic models and summarizes them. These findings, together with context-related factors, are important to consider when deciding if, how, and for whom a personalized prognostic tool should be created and implemented. We present a checklist of questions and an Excel sheet calculation table, allowing researchers to weigh the benefits and harms of creating and implementing a personalized prognostic model on a population level against one-size-fits-all standard care in a structured and standardized manner. We condense this into a single value using a uniform Benefit-Risk-Score (BRS) formula. Together with context-related factors, the calculation table and formula are designed to aid researchers in their decision-making process on providing a personalized prognostic tool and to decide for (or against) its complete or partial implementation. This work serves as a foundation for further discourse and refinement of tool development decisions for prognostic models in healthcare.

Research efforts are growing rapidly in the digital health industry, but with this growth comes increasing ethical challenges. In this viewpoint paper, we leverage over 20 years of combined experience across academia, industry, and digital health to address critical issues related to ethics, specifically privacy policies and institutional review board (IRB) compliance, which are often misunderstood or misapplied. We examine the purpose of privacy policies and IRBs, provide brief examples where companies faced legal and ethical consequences due to shortcomings, and clarify common misconceptions. Finally, we offer recommendations for digital health companies to improve their ethical practices and ensure compliance in a rapidly evolving landscape.

The escalating prevalence of obesity worldwide increases the risk of chronic diseases and diminishes life expectancy, with a growing economic burden necessitating urgent intervention. The existing tiered approach to weight management, particularly specialist tier 3 services, falls short of meeting the population’s needs. The emergence of digital health tools, while promising, remains underexplored in specialized National Health Service weight management services (WMSs).

The use of health-related online peer support groups to support self-management of health issues has become increasingly popular. The quality of information and advice may have important implications for public health and for the utility of such groups. There is some evidence of variable quality of web-based health information, but the extent to which misinformation is a problem in online peer support groups is unclear.

Emergency departments (EDs) face significant challenges due to overcrowding, prolonged waiting times, and staff shortages, leading to increased strain on health care systems. Efficient triage systems and accurate departmental guidance are critical for alleviating these pressures. Recent advancements in large language models (LLMs), such as ChatGPT, offer potential solutions for improving patient triage and outpatient department selection in emergency settings.