JMIR Formative Research

University students experience elevated psychological distress, with limited access to mental health services. While cognitive behavioral therapy (CBT) demonstrates efficacy for anxiety and depression, treatment gaps persist due to access barriers and insufficient between-session support. Large language model (LLM) chatbots could improve and scale CBT delivery. However, the scientific evaluation of chatbot-enhanced protocols is just emerging.

Adolescent anxiety is a growing public health concern associated with significant social and emotional impairment. Mindfulness-based interventions (MBIs) have shown promise in reducing anxiety and improving well-being; however, engagement remains challenging. Virtual reality (VR)–based delivery may enhance immersion and attention, potentially addressing barriers of traditional mindfulness formats. Evidence on VR-based mindfulness interventions for adolescents, particularly in Hong Kong, remains limited.

Delirium superimposed on dementia is associated with poor outcomes yet remains underdetected in home settings. Current detection relies on face-to-face clinical assessment (eg, the Confusion Assessment Method criteria), which is rarely applied outside hospitals.

Fluid assessment in geriatric inpatients is challenging, as clinical signs are often unreliable. Inferior vena cava (IVC) ultrasound provides a rapid, noninvasive estimation of intravascular volume. Teleguided point-of-care ultrasound (POCUS) allows examiners without prior ultrasound experience to perform scans under real-time supervision.

Clinical dashboards are becoming important tools for managing and monitoring hospitalized patients across different wards. Moreover, careful attention to design, usability, and user interaction is essential for developing effective support tools for clinicians.

Stress among health care workers (HCWs) contributes to burnout, workforce attrition, and adverse patient outcomes. Although virtual reality (VR), psychoeducation, ecological momentary assessments (EMAs), and wearables have independently shown promise in stress research, no integrated digital suite has combined controlled stress induction, intervention delivery, and longitudinal real-world monitoring in HCWs.

Depression is a prevalent mental disorder, and it remains one of the leading causes of disability in Canada and globally. Mobile app–based physical activity interventions may offer an effective and accessible treatment option for individuals with depression who cannot or prefer not to access supervised exercise programs.

People with intellectual disability experience rates of mental illness up to 3 times higher than the general population, yet face significant barriers to care, including limited clinician expertise, diagnostic overshadowing, and exclusion from key mental health services. Electronic mental health interventions have demonstrated effectiveness in the general population and may address these barriers for people with intellectual disability by providing accessible, tailored treatment.

Primary health care (PHC) delivery in Pakistan is constrained by persistent workforce shortages, which are further exacerbated by the attrition of trained female physicians following marriage or childbirth. Telehealth platforms, such as Sehat Kahani, have emerged as one response to this gap, enabling female physicians to provide remote primary care from home. Within this model, a digital decision support system (DDSS) was recently piloted for selected febrile illnesses to strengthen clinical decision-making. However, evidence on how such systems are perceived by female PHC providers in low-income and middle-income country settings remains limited. In particular, there is limited understanding of how perceived usefulness, ease of use, and perceived impact on quality of care shape the early adoption of DDSS within tele-PHC workflows.

Postpartum hemorrhage requiring a blood transfusion is a concern for patients and clinicians; its risk and the mode of delivery are important points of discussion before labor. Many high-risk factors associated with postpartum hemorrhage are known prior to delivery and are often unpreventable. Delivery plans are influenced by the patient’s medical history, their preferences, and clinical decision-making. Informed consent regarding known risk factors for postpartum hemorrhage will help guide delivery care plans and mitigate risk. Machine learning models have been used to predict postpartum hemorrhage; however, translation into clinical support tools is challenging. Shared decision-making discussions can be facilitated with machine learning model–based clinical support tools predicting postpartum hemorrhage requiring a transfusion.

Automated machine learning (AutoML) frameworks can lower technical barriers for predictive and prognostic model development in radiomics by enabling researchers without programming expertise to build models. However, their effectiveness in addressing radiomics-specific challenges remains unclear.

Climate change is expected to cause more than 250,000 deaths annually by 2050 and could increase the prevalence of asthma and chronic obstructive pulmonary disease (COPD) by up to 30%. Pressurized metered-dose inhalers (pMDIs), primarily delivering short-acting beta-2 agonists, generate 15 to 30 times more greenhouse gas emissions than dry powder or soft mist inhalers. In France, short-acting beta-2 agonist pMDIs account for 95% of reliever therapy prescriptions, despite their limited effectiveness in controlling disease symptoms.