Journal of Medical Internet Research

mHealth interventions can produce both intended and unintended effects. Examining these unintended effects helps to create a more complete and objective understanding of mHealth interventions and can reduce potential harm to participants. Existing studies on the unintended effects, which were published several years ago, tend to have either a general focus on health information technology or a specific focus on healthcare providers, thereby excluding other key stakeholders (e.g., patients, community health workers). Additionally, these studies did not systematically outline the causes of the unintended effects or strategies for their prevention.

Ineffective postoperative pain management affects more than 25% of hospitalized children, leading to increased morbidity, impaired physical function, delayed recovery, prolonged opioid use, and heightened healthcare costs. Traditional pharmacological interventions have limitations, particularly given growing concerns over long-term opioid use in pediatric populations. Virtual reality (VR) has emerged as a promising non-pharmacological intervention for pediatric pain management, offering immersive, multisensory experiences that can effectively distract and engage patients' attention away from pain sensations. This viewpoint examines the current evidence and prospects for VR as a component of pediatric multimodal pain management strategies. Several VR modalities have shown potential for reducing pain and anxiety in pediatric populations, including distraction-based VR therapy (VR-D), exposure-based VR therapy (VR-E), guided relaxation-based VR therapy (VR-GR), and biofeedback-based VR therapy (VR-BF). The neurobiological underpinnings of VR's analgesic effects involve multiple mechanisms: the gate control theory explains how intense multisensory VR inputs compete with pain signal transmission, while the attention-modulation pathways involving the anterior cingulate cortex and periaqueductal gray work alongside emotional regulation pathways through amygdala connections to reduce pain perception. Recent studies involving various pediatric surgical populations have demonstrated VR's potential to reduce postoperative pain intensity, pain unpleasantness, anxiety, and in some cases, the need for rescue analgesia. However, VR's analgesic effects appear to be transient, typically lasting 15-30 minutes, which suggests the need for optimization in timing and frequency of VR sessions. Implementation challenges include cost considerations, technological access disparities, logistical requirements for safe use and storage, and staff training needs. As hospitals and healthcare systems continue to explore non-pharmacological pain management strategies, VR represents a promising adjunct to traditional approaches, potentially reducing reliance on opioid medications while improving patient experience and outcomes. Throughout this review, we address the major concepts related to VR, the use of VR in differing clinical situations, various VR-based therapy methods, and the practicality of VR to alleviate pain, as well as several key findings to date and future directions.

Real-world data (RWD) are increasingly used in health research and regulatory decision-making to assess the effectiveness, safety, and value of interventions in routine care. However, the heterogeneity of European healthcare systems, data capture methods, coding standards, and governance structures poses challenges for generating robust and reproducible real-world evidence (RWE). The European Health Data & Evidence Network (EHDEN) was established to address these challenges by building a large-scale, federated data infrastructure that harmonizes RWD across Europe.

The integration of artificial intelligence (AI) in health care has significant potential, yet its acceptance by health care professionals (HCPs) is essential for successful implementation. Understanding HCPs’ perspectives on the explainability and integrability of medical AI is crucial, as these factors influence their willingness to adopt and effectively use such technologies.

Electronic health record (EHR) data is widely used in public health research, including in HIV-related studies, but are limited by potential bias due to incomplete and inaccurate information, lack of generalizability, and lack of representativeness.

Urinary incontinence (UI) is a common condition during pregnancy, significantly impacting their physical and mental well-being as well as quality of life. With advancements in mobile health (mHealth) technology, mobile applications provide innovative approaches for managing UI. Although small-scale studies have demonstrated their efficacy in alleviating maternal UI symptoms, there is a notable lack of large-scale, multicenter trials to validate these findings.

In the United States, the COVID-19 pandemic accelerated the adoption of telehealth in home health care (HHC), but its sustainability remains uncertain. Despite telehealth’s potential benefits, including improved patient monitoring and expanded access, the lack of reimbursement and regulatory constraints may limit widespread adoption. Understanding how home health agencies (HHAs) perceive these challenges is critical for shaping future telehealth policy.

Heart rate variability (HRV) indicates brain-body interaction and has been associated with a variety of mental and physical health indicators. Transient reductions in HRV, independent of bodily movement (so-called additional HRV reduction [AddHRVr]), may reflect moments of psychophysiological vulnerability. This metric is quantified by regressing bodily movement on the root mean square of successive differences and identifying reductions <0.5 SD of the predicted value in real time in everyday life.

In the era of internet-based governance, online public appeals—particularly in the healthcare domain—have become a vital channel for expressing citizens’ demands.

The growing ubiquity of digital footprint data presents new opportunities for behavioural epidemiology and public health research. Among these, supermarket loyalty card data—passively collected records of consumer purchases—offer objective, high-frequency insights into health-related behaviours at both individual and population levels. This article explores the potential of loyalty card data to strengthen public health surveillance across four key behavioural risk domains: diet, alcohol, tobacco, and over-the-counter medication use. Drawing on recent empirical studies, we outline how these data can complement traditional epidemiological data sources by improving exposure assessment, enabling real-time trend monitoring, and supporting intervention evaluation. We also discuss critical methodological challenges, including issues of representativeness, data integration, and privacy, as well as the need for robust validation strategies. By synthesising the current evidence base and offering practical recommendations for researchers, this paper highlights how loyalty card data can be responsibly leveraged to advance behavioural risk monitoring and support the adaptation of epidemiological practice to contemporary digital data environments.

As digital technology becomes increasingly embedded in daily life, digital isolation among older adults has become more pronounced. This isolation may restrict access to health information and social support, potentially leading to poorer sleep quality. However, most existing studies on digital isolation and sleep disorders were cross-sectional, lacking longitudinal evidence to establish causality.

The potential for generative AI (GenAI) to assist with clinical tasks is the subject of ongoing debate within biomedical informatics and related fields.