Journal of Medical Internet Research

Clinical guideline development preferentially relies on evidence from randomized controlled trials (RCTs). RCTs are gold-standard methods to evaluate the efficacy of treatments with the highest internal validity but limited external validity, in the sense that their findings may not always be applicable to or generalizable to clinical populations or population characteristics. The external validity of RCTs for the clinical population is constrained by the lack of tailored epidemiological data analysis designed for this purpose due to data governance, consistency of disease or condition definitions, and reduplicated effort in analysis code.

Investigating the safe range of orthodontic tooth movement is essential for maintaining oral and maxillofacial stability posttreatment. Although clear aligners rely on pretreatment digital models, their effect on periodontal hard tissues remains uncertain. By integrating cone beam computed tomography–derived cervical and root data with crown data from digital intraoral scans, a 3D fusion model may enhance precision and safety.

A greater understanding of the effectiveness of digital self-management programs and their ability to support longer-term weight loss is needed.

Health inequalities among older adults become increasingly pronounced as aging progresses. In the digital era, some researchers argue that access to and use of digital technologies may contribute to or exacerbate these existing health inequalities. Conversely, other researchers believe that digital technologies can help mitigate these disparities.

Longitudinal cohort studies have traditionally relied on clinic-based recruitment models, which limit cohort diversity and the generalizability of research outcomes. Digital research platforms can be used to increase participant access, improve study engagement, streamline data collection, and increase data quality; however, the efficacy and sustainability of digitally enabled studies rely heavily on the design, implementation, and management of the digital platform being used.

Some scholars who are skeptical about open-access mega journals (OAMJs) have argued that low-quality papers are often difficult to publish in more prestigious and authoritative journals, and OAMJs may be their main destination.

User trust is pivotal for the adoption of digital health systems interventions (DHI). In response, numerous trust-building guidelines have recently emerged targeting DHIs such as artificial intelligence. The common aim of these guidelines aimed at private sector actors and government policy makers is to build trustworthy DHI. While these guidelines provide some indication of what trustworthiness is, the guidelines typically only define trust and trustworthiness in broad terms, they rarely offer guidance about economic considerations that would allow implementers to measure and balance trade-offs between costs and benefits. These considerations are important when deciding how best to allocate scarce resources (eg, financial capital, workforce, or time). The missing focus on economics undermines the potential usefulness of such guidelines. We propose the development of actionable trust-performance-indicators (including but not limited to surveys) to gather evidence on the cost-effectiveness of trust-building principles as a crucial step for successful implementation. Furthermore, we offer guidance on navigating the conceptual complexity surrounding trust and on how to sharpen the trust discourse. Successful implementation of economic considerations is critical to successfully build user trust in DHI.

Monitoring vital signs in hospitalized patients is crucial for evaluating their clinical condition. While early warning scores like the modified early warning score (MEWS) are typically calculated 3 to 4 times daily through spot checks, they might not promptly identify early deterioration. Leveraging technologies that provide continuous monitoring of vital signs, combined with an early warning system, has the potential to identify clinical deterioration sooner. This approach empowers health care providers to intervene promptly and effectively.

Heart failure (HF) is one of the most common causes of hospital readmission in the United States. These hospitalizations are often driven by insufficient self-care. Commercial mobile health (mHealth) technologies, such as consumer-grade apps and wearable devices, offer opportunities for improving HF self-care, but their efficacy remains largely underexplored.

Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients’ prognosis.

With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment methods are more effective than traditional methods such as newspapers, media, or flyers is inconsistent. Here we present insights from our experience recruiting tertiary education students for a digital mental health artificial intelligence–driven adaptive trial—Vibe Up.

In online mental health communities, the interactions among members can significantly reduce their psychological distress and enhance their mental well-being. The overall quality of support from others varies due to differences in people’s capacities to help others. This results in some support seekers’ needs being met, while others remain unresolved.