JMIR Formative Research

Unplanned hospital readmissions represent a critical operational and financial challenge for health care systems in the United States, with 3.8 million 30-day all-cause readmissions in 2018 at an average cost of US $15,200 each, totaling US $58 billion in costs. Many published prediction models rely on comprehensive information (eg, full billing abstractions, discharge summaries, laboratory tests, and vitals) that becomes available only late in the encounter, limiting usefulness for real-time, in-hospital intervention. This creates a timeliness-accuracy trade-off: models that are most accurate retrospectively may arrive too late to act upon.

Hypertension is a significant risk factor for cardiovascular diseases and premature mortality, with its prevalence increasing due to population aging and lifestyle factors. Accurate measurement of blood pressure (BP) and arterial oxygen saturation is crucial for disease prevention and monitoring, and wearable devices have emerged as a promising alternative. However, their clinical reliability requires validation, particularly in older populations.

Preterm infants (PTIs) are at increased risk of neurodevelopmental impairment, particularly in socioeconomically vulnerable regions, where access to early intervention services is limited. However, little is known about the feasibility of longitudinal developmental monitoring in such contexts.

Population aging is driving a rapid rise in dementia cases worldwide, posing a major challenge for health care systems around the globe, including in the Netherlands. Digital multidomain lifestyle interventions, which target multiple lifestyle domains simultaneously, can protect against cognitive decline in at-risk older adults but struggle to sustain engagement. Addressing stress in these interventions is crucial, as it can directly increase dementia risk and may promote unhealthy behaviors in other domains targeted in these interventions, including physical activity, diet, and sleep.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with low-dose computed tomography screening demonstrating an approximately 20% reduction in mortality among high-risk individuals. Despite this benefit, screening prevalence remains suboptimal, with often less than 20% of eligible individuals reported to be up to date on screening. Shared decision-making is essential for effective lung cancer screening (LCS) implementation, with decision aids shown to enhance patient knowledge and engagement.

Internet-based interventions for insomnia show promise, but understanding the process variables, such as knowledge acquisition and psychological acceptance, is crucial for enhancing digital adherence and clinical effectiveness.

Quality of life (QOL) is an important aspect of every chronic disease, including interstitial lung disease (ILD). QOL is perceived as a significant patient-centered outcome.

The Technology Adoption Model (TAM) offers a potential framework for elucidating the relationships between data privacy or security concerns and behavioral intention, perceived usefulness (PU), and perceived ease of use (PEOU) of mobile health (mHealth) apps, particularly for patients’ self-care management. In Saudi Arabia, limited information is available on these pertinent research areas despite the government’s relentless efforts to bolster the use of mHealth apps.

Somatic symptom disorder (SSD) is a mental disorder marked by persistent somatic symptoms and maladaptive health-related thoughts, feelings, or behaviors. Cognitive behavioral therapy has been shown to be effective in treating SSD, reducing patients’ somatic symptoms, depressive symptoms, and anxiety symptoms. However, challenges remain—including limited access to treatment. Videoconference-based cognitive behavioral therapy (vCBT) has emerged as a promising approach, offering flexible and tailored treatment while addressing the shortage of medical resources and potentially reducing patient dropout.

Simple behavioral and cognitive actions can reduce symptoms of anxiety and depression. However, there is limited research investigating whether restricting those same actions increases symptoms and whether resuming those actions reduces symptoms.

Substance use disorder (SUD) remains a major public health crisis in the United States, with significant challenges in treatment access, retention, and workforce capacity. SUD care teams, including addiction medicine physicians and peer recovery coaches (PRCs), support patients receiving SUD treatment but face heavy workloads and burnout. Artificial intelligence (AI) innovations, particularly large language model (LLM)–based chatbots, may extend PRC support and provide patients with on-demand recovery support between clinic visits and PRC contacts. However, evidence on their development, feasibility, acceptability, and usability in addiction services remains limited.

Parkinson disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with PD, informal caregivers, and health care professionals. With growing interest in digital and predictive artificial intelligence (AI) tools for disease management, understanding the needs and digital readiness of these stakeholder groups is crucial.