Journal of Medical Internet Research

Digital technologies are reshaping human behavior, health care delivery, and population health; however, their cumulative effects across the lifespan remain underexplored. This viewpoint argues that exposures arising from interactions with digital technologies should be formally integrated into exposome science as a distinct, measurable component of the human environment. Our aims are to (1) redefine the digital component of the exposome (the digital exposome) within the broader exposome framework, (2) examine its life course implications for health and equity, and (3) outline a research and policy agenda to enable its systematic measurement and integration into clinical and public health practice. Digital technology–related exposures can confer benefits such as enhanced health monitoring, personalized interventions, improved access to care, and the promotion of healthy behaviors. However, they may also introduce potential risks, including mental health challenges, cognitive and circadian disruptions, sedentary lifestyles, exposure to misinformation, and widening inequities among vulnerable populations. Despite their ubiquity, digital technology–related exposures remain poorly integrated into clinical medicine, epidemiology, or public and global health policies. Drawing on interdisciplinary evidence from exposure science, epidemiology, and digital phenotyping research, we propose a refined conceptual definition of the digital exposome grounded in the classical exposome domains. We propose redefining the digital exposome as the full spectrum of exposures resulting from interactions or proximities with digital technologies and their combined influence on health across the lifespan. This framework conceptualizes digital technology–related exposures as a dynamic set of environmental influences operating through sociotechnical, behavioral, and biological pathways over the life course. To operationalize this framework, we discuss practical approaches using validated behavioral instruments, objective device use logs, ecological momentary assessments, smartphone-based digital phenotyping, and wearable sensing technologies. Systematic measurement, large-scale longitudinal studies, and harmonized exposure metrics are needed to characterize the cumulative health impacts of digital environments more accurately. Emerging tools such as digital markers or biomarkers and digital phenotypes offer promising opportunities to link real-world technology use with physiological and biological outcomes, thereby supporting precision medicine and population health strategies. Ethical governance, privacy safeguards, and equity considerations must be embedded from the start, drawing on emerging exposomethics frameworks. Recognizing the digital exposome as a modifiable determinant of health offers a foundation for evidence-based guidance, prevention strategies, and policy interventions suited to increasingly digital societies. By integrating digital technology–related exposures into exposome science, clinical practice, and public health research, this viewpoint seeks to foster interdisciplinary dialogue, guide future empirical work, and support the development of safer and more equitable digital environments across the lifespan.

Health care innovation is essential for improving patient outcomes, enhancing system efficiency, and preparing for future challenges; however, meaningful progress is often hindered by entrenched barriers such as resistance to change, fragmented interdisciplinary collaboration, and constrained financial and human resources. These persistent obstacles make it difficult for health care systems to translate creative ideas into sustainable, real-world improvements, underscoring the need for structured approaches that support collaboration and reduce implementation friction. To address these challenges, we developed the Actionable Innovation Day (AID) approach, a structured, participatory model designed to generate consensus-based, low-cost recommendations that are feasible for system improvement. The first regional AID event in Eastern Ontario gathered 57 multidisciplinary participants, including clinicians, administrators, patient partners, and industry leaders, for a full-day series of presentations, facilitated discussions, and targeted breakout sessions focused on critical care. Through guided deliberation and collaborative analysis, participants synthesized diverse perspectives into a prioritized set of improvement opportunities. The process yielded 28 actionable recommendations across 4 domains: health care innovation, regionalized care, critical care practices, and the use of artificial intelligence. A postevent survey (86% response rate) showed strong agreement, with 23 recommendations rated above 4 on a 5-point scale. The highest-ranked proposals emphasized the value of strengthening research-industry-clinical partnerships, integrating families more intentionally into intensive care unit rehabilitation and recovery processes, and implementing centralized regional coordination to optimize critical care capacity. Together, these findings illustrate not only the feasibility of the AID model but also the AID model’s ability to surface strategic, context-appropriate solutions that resonate across stakeholder groups. The AID process offers a scalable and adaptable template for advancing health care innovation through collaborative, real-world problem-solving. While this initial event focused on critical care, the underlying principles of structured engagement, iterative consensus building, and interdisciplinary co-design are broadly applicable to many sectors of health care. We encourage institutions, regional networks, and health system leaders to adopt and tailor the AID framework to their own local priorities, recognizing that inclusive innovation processes can accelerate system improvement even in resource-limited settings. Ultimately, the AID approach serves as both a methodology and a call to action: by empowering teams to collectively identify, refine, and champion actionable ideas, health care organizations can build the capacity and culture necessary to drive meaningful and sustained innovation across diverse clinical and operational domains.

Loneliness is a widespread public health concern linked to increased risks of health problems. As populations age, the demand for effective interventions to mitigate loneliness continues to grow.

In this Viewpoint, we argue that patient-facing high-fidelity artificial intelligence (AI)–generated video requires governance that is operational, life cycle based, and embedded in existing institutional review pathways rather than limited to predeployment checks alone. Patient-facing high-fidelity AI-generated video—synthetic or substantially AI-mediated video that presents realistic human likeness, voices, or clinical communication cues—is rapidly entering patient education and clinical communication. We propose a risk-and-ethics matrix that combines residual clinical risk (likelihood × severity after mitigations) with an ethical alignment score that operationalizes autonomy, beneficence, nonmaleficence, and justice to yield actionable dispositions (encourage, permit with oversight, restrict or redesign, or prohibit). The framework links each disposition to dossier-based review, minimum controls, and postdeployment monitoring triggers—focused on measurable outcomes (eg, comprehension, content-attributable follow-up burden, incidents and complaints, and equity gaps) as well as provenance and change control—to support auditable, revisitable decisions over the system life cycle.

Artificial intelligence (AI) in the domain of health care is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI.

Cognitive behavioral therapy (CBT) is the most examined psychotherapy for depression and anxiety, but delivery faces significant barriers such as limited access, cost, and time constraints. CBT-oriented psychological chatbots offer a promising means of addressing these challenges. Yet, their overall efficacy, user engagement, and acceptability have not been systematically synthesized.

Diabetes technologies—including continuous glucose monitoring (CGM), insulin pumps, and hybrid closed-loop systems—have profoundly transformed self-management in type 1 diabetes (T1D). While these technologies offer improved glycemic control and safety, their use in ultraendurance sports introduces specific cognitive, material, and organizational challenges that remain underexplored in digital health research.

Social media platforms offer extensive data, as they are widely used globally. Social media mining (SMM) enables real-time monitoring of user-reported health information and serves as a supplement to traditional health data analytics. However, the rapid proliferation of literature has produced fragmentation, and a comprehensive knowledge map regarding SMM is lacking. Further, existing bibliometric reviews in health fields are easily undermined by synonym fragmentation and parameter settings, reducing their robustness. Thus, a more robust, reproducible, and decision-oriented bibliometric framework is required.

Patient education materials (PEMs) often exceed the American Medical Association’s (AMA) recommended sixth-grade reading grade level (RGL). While artificial intelligence (AI) offers potential for automated text simplification, concerns persist regarding linguistic quality, content fidelity, and the understandability of simplified PEMs by laypeople.

Generative artificial intelligence (GenAI) is enhancing virtual patient simulations in health care education by enabling dynamic, adaptive interactions, reshaping how clinical skills are taught. A synthesis of the current evidence is needed to guide implementation and future research, given the pace of technological advancement.

Mobile health (mHealth) technologies have gained popularity and may play a key role in promoting health-related behaviors. However, the impact of mHealth interventions on work-related outcomes remains unclear.

This commentary argues that for low-intensity postdischarge interventions, emergency department use may be a more sensitive and appropriate indicator of transitional care quality than readmission. It also positions nurse-led telephone follow-up as interpretive, equity-sensitive transitional care work that helps patients make discharge plans actionable in the home context while highlighting the value of accessible, scalable digital modalities such as telephone outreach.