JMIR Human Factors

In 2019, Germany introduced a unique regulatory framework for digital therapeutics (DTx), termed digital health applications (DiGAs) in Germany, with the goal of integrating evidence-based DTx into statutory health care. DTx are eligible for reimbursement by statutory health insurance if manufacturers demonstrate positive health care effects, such as improved health status or health literacy, in a controlled study setting. Although regulatory evaluation primarily relies on manufacturer-conducted studies, these studies do not fully capture how users experience and use DiGAs in everyday life.

Safe integration of AI-enabled clinical decision support requires understanding whether users’ trust and behavioral reliance are appropriately calibrated to recommendation quality.

Inclusive physical education (PE) plays a vital role in promoting participation and development among students with different abilities. However, many teachers do not have adequate tools to modify PE activities to meet these diverse needs. In addition, parents are essential partners, as their involvement helps reinforce strategies and provide useful information about their children. While online platforms provide a practical way to deliver such solutions, only a few have been intentionally created to support both teachers and parents in implementing inclusive PE learning. To address this gap, we codeveloped an online platform with adapted PE experts that digitizes an inclusion screening tool and delivers personalized, evidence-based inclusion strategies and resources to teachers and parents.

Lung-protective ventilation (LPV) reduces complications of mechanical ventilation, yet adherence in intensive care units (ICUs) remains inconsistent. Digital dashboards may support LPV by improving situational awareness and supporting protocol adherence. However, adoption of such tools in high-acuity clinical environments depends on a range of cognitive, professional, and contextual determinants. The Measurement Instrument for Determinants of Innovations (MIDI) provides a validated framework to systematically assess these factors.

Online medical consultation (OMC) services have gained considerable attention as an integral component of telemedicine. Recently, AI has been increasingly integrated into OMC platforms, facilitating more efficient consultations and clinical decision-making. AI-driven OMC services can provide preliminary triage, medication guidance, and diagnostics for multiple medical conditions. Despite the availability and potential benefits of AI-driven OMC services, public acceptance and willingness to pay (WTP) for these services remain low.

Digital health platforms can expand access to HIV care, but among men who have sex with men (MSM) and transgender people living with HIV and AIDS in Nigeria, adoption is shaped by structural stigma, criminalization, and fear of disclosure as much as by system functionality. Teleconsultation and medication-delivery platforms offer alternative pathways to care, but their acceptance within marginalized populations cannot be assumed and requires empirical investigation.

Telemedicine adoption among health care professionals is hindered by resistance to change (RTC), comprising routine-seeking, emotional reactivity, short-term focus, and cognitive rigidity, which interacts with perceived ease of use (PEU) and anxiety within the Technology Acceptance Model framework.

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AI has the potential to enhance clinical decision-making in high-acuity settings such as intensive care units (ICUs) and emergency departments (EDs). However, despite promising performance, many AI-driven clinical decision support systems (AI-CDSSs) face poor adoption due to issues of trust, workflow disruption, and alert fatigue. Understanding the human factors that shape clinician acceptance is critical to guide safe and effective implementation of AI-CDSS in acute care. Theoretical frameworks, including the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model and the technology acceptance model (TAM), suggest that successful adoption requires addressing sociotechnical interactions among clinician trust, system design, organizational readiness, and task complexity, yet few empirical studies have applied these frameworks to AI-CDSSs in acute care settings.

Digital health interventions for psychosis, like SloMo, leverage smartphone technology to help transfer learning from therapy to real-life situations. Usage relies on motivation, recall, and awareness. Wearable devices that track physiological signs of stress can boost engagement by encouraging the use of coping strategies when most needed.

Augmented reality (AR) glasses with waveguide optics and facial recognition may enhance patient identification and clinical workflows. However, their usability among physicians has not yet been evaluated.