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 Rachele Hendricks-Sturrup, DHSc, MSc, MA, FACTS, Lead Editor; Research Director of Real-World Evidence, Duke-Margolis Institute for Health Policy, Washington, DC
Impact Factor 8.2 More information about Impact Factor CiteScore 10.4 More information about CiteScore
Recent Articles
Social media use disorder (SMUD) has become an important concern in adolescent digital health. Although prior research has linked problematic social media use to psychological and family-related factors, less is known about how academic stress and different patterns of family relationships are associated with SMUD among middle school students.
The COVID-19 pandemic transformed telemedicine in Poland from a niche service into a core mode of health care delivery, supported by nationwide eHealth infrastructure such as obligatory e-prescriptions and the central P1 platform. Despite this rapid expansion, concerns persist that the benefits of digital health are not shared equally, and that a digital divide limits equitable access to telemedicine services across sociodemographic and geographic groups.
AI is increasingly being integrated into diabetes care, with growing evidence supporting its potential to improve clinical decision-making, risk prediction, and self-management. However, the lived experiences, expectations, and concerns of those involved in its implementation have not been adequately synthesized.
Unobtrusive digital monitoring technologies (UDMTs) enable continuous data collection beyond episodic clinical encounters and are increasingly incorporated into digital health clinical trials. However, their successful use depends on their integration into routine clinical practice. Poor integration can increase hidden nursing workload, disrupt workflows, compromise data quality, and limit the sustainability of digital trials. Although nurses play a central role in coordinating clinical, research, and technological activities, little is known about how they engage with UDMT-enabled clinical trials in everyday practice.
Vascular compromise remains the leading cause of free-flap failure. AI-based monitoring and prediction tools have emerged as a promising adjunct for postoperative free flap monitoring and early detection of vascular compromise. Previous systematic reviews included limited evidence or broadly evaluated reconstructive outcomes.
Traditionally, the number and location of cerebral microbleeds (CMBs) are manually calculated based on magnetic resonance imaging (MRI) characteristics such as shape, size, and signal features. Although accurate, manual detection requires expert interpretation and is costly. Therefore, it is necessary to explore an effective auxiliary detection method. In recent years, deep learning (DL) has been increasingly used in the detection of cerebral hemorrhage. Some studies have explored image-based DL models for diagnosing CMBs. Nevertheless, systematic evidence regarding their diagnostic accuracy is lacking.
The integration of digital health technology (DHT) into chronic kidney disease (CKD) care holds transformative potential for enhancing patient self-management and slowing disease progression. Despite the growing availability of DHT, there remains limited understanding of the factors that facilitate or hinder their adoption and use among patients with CKD.
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