Journal of Medical Internet Research
The leading peer-reviewed journal for digital medicine and health and health care in the internet age.
Editor-in-Chief:
Gunther Eysenbach, MD, MPH, FACMI, Founding Editor and Publisher; Adjunct Professor, School of Health Information Science, University of Victoria, Canada
Impact Factor 5.8 CiteScore 14.4
Recent Articles
Digital health interventions targeting behavior change are promising in adults and adolescents; however, less attention has been given to younger children. The proliferation of wearables, such as smartwatches and activity trackers, that support the collection of and reflection on personal health data highlights an opportunity to consider novel approaches to supporting health in young children (aged 5-11 y).
Immersive virtual reality (iVR) has emerged as a training method to prepare medical first responders (MFRs) for mass casualty incidents (MCIs) and disasters in a resource-efficient, flexible, and safe manner. However, systematic evaluations and validations of potential performance indicators for virtual MCI training are still lacking.
An effective primary treatment for obsessive-compulsive disorder (OCD) in children and adolescents as well as adults is exposure and response prevention (ERP), a form of intervention in the context of cognitive-behavioral therapy. Despite strong evidence supporting the efficacy and effectiveness of ERP from studies in research and real-world settings, its clinical use remains limited. This underuse is often attributed to access barriers such as the scarcity of properly trained therapists, geographical constraints, and costs. Some of these barriers may be addressed with virtual behavioral health, providing ERP for OCD through video teletherapy and supplemented by app-based therapeutic tools and messaging support between sessions. Studies of teletherapy ERP in adults with OCD have shown benefits in research and real-world settings in both small and large samples. However, studies of teletherapy ERP in children and adolescents thus far have been in small samples and limited to research rather than real-world settings.
Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
Sarcopenia is closely associated with a poor quality of life and mortality, and its prevention and treatment represent a critical area of research. Resistance training is an effective treatment for older adults with sarcopenia. However, they often face challenges when receiving traditional rehabilitation treatments at hospitals.
Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes.
Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection remains challenging due to its insidious symptoms. Current diagnostic methods, including clinical assessments and laboratory tests, frequently lack the speed and specificity needed for timely intervention, particularly in vulnerable populations such as older adults, intensive care unit (ICU) patients, and those with compromised immune systems. While bacterial cultures remain vital, their time-consuming nature and susceptibility to false negatives limit their effectiveness. Even promising existing machine learning approaches are restricted by reliance on complex clinical factors that could delay results, underscoring the need for faster, simpler, and more reliable diagnostic strategies.
Digital technologies for type 2 diabetes mellitus (T2DM) care hold great potential to improve patients’ health in the long term. Only a subset of telemedicine offerings are digital interventions that meet the criteria for prescribable digitale Gesundheitsanwendung (digital health apps; DiGAs) in Germany. Digital treatments further provide vast amounts of patient data that are important to generate evidence.
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