JMIR Formative Research

As the global population ages, Parkinson disease (PD) has emerged as the second most prevalent neurodegenerative condition after Alzheimer disease. People with PD often experience speech problems, including reduced volume, monotone pitch, breathiness, and word slurring. Interventions such as speech therapy through mobile apps provide reassurance and easier access to care for patients having this neurodegenerative condition. Mobile apps offer patients with PD greater access to care and the reassurance of being able to manage their condition at home. However, we do not know the quality of these apps. A systematic evaluation of these mobile apps is necessary to ensure their effectiveness and suitability for use by patients with PD.

Advance care planning (ACP) is an ongoing dialogical process. Despite growing policy-level recognition, ACP engagement remains limited in Japan, and many individuals remain unfamiliar with the concept. Beyond knowledge deficits, insufficient psychological readiness may also impede engagement. Evidence on fully online narrative- and game-based approaches to supporting such readiness remains limited.

Obesity imposes a substantial economic burden, accounting for an estimated 10% of total health care expenditures in Germany. Digital health apps (DHAs) have demonstrated effectiveness in supporting weight management among individuals with obesity; however, evidence regarding their long-term economic impact remains limited.

Natural language processing and large language model systems are increasingly used to support mental health documentation, screening, and follow-up planning. In counseling contexts, model outputs may influence diagnostic framing, risk recognition, and clinical record content. Static performance metrics and fluent generated summaries are not sufficient to support safe implementation without governance, safety gating, human review, and monitoring.

Cesarean section (C-section) is the most common surgical procedure in the United States, yet its use varies widely across regions and institutions. Although clinical risk factors are central to delivery decisions, geographic context, health system capacity, and local practice patterns may also influence C-section use. Understanding both the determinants and predictability of C-section delivery is important for improving obstetric quality and equity.

Multicall memory capabilities in AI-powered health care communication systems show promise for enhancing patient engagement, but their impact on engagement and patient satisfaction remains unclear.

Crystal methamphetamine (“ice”) is a drug of significant concern across Australia; however, access to culturally appropriate, community-led, and co-developed resources is limited for Aboriginal and Torres Strait Islander communities.

Hospital IT outages severely disrupt clinical workflows and use of electronic medical records, threatening patient safety and operational continuity. Traditional disaster response training faces limitations, including high resource requirements, restricted repeatability, and inability to be conducted without interrupting 24/7 hospital operations. Digital twin technology enables realistic, repeatable simulation training in virtual environments, avoiding operational disruption.

Literature reviews rely on rigorous title and abstract screening by researchers, which is time-consuming. AI-assisted literature screening tools have been proposed to improve efficiency by prioritizing titles and abstracts with the highest likelihood of meeting the inclusion criteria, thereby reducing the need to screen all records.

Cohort selection criteria play a critical role in shaping machine learning (ML) model performance and the equity of clinical outcome predictions across demographic groups. In practice, cohort definitions are often influenced by variable and sometimes inconsistent data processing decisions, which may introduce bias and limit the generalizability of ML models. During the COVID-19 pandemic, rapid cohort construction further increased concerns about transparency and fairness in ML-based analyses.

Neighborhood disinvestment, characterized by built environment disrepair and deterioration, has been linked to health behaviors and outcomes, including cancer survival. However, disinvestment temporal dynamics, including time-lagged exposure estimates among colorectal cancer (CRC) cases, remain underexplored.

Parent management training (PMT) is an evidence-based intervention for addressing child behavioral difficulties; however, caregivers often need additional guidance when implementing skills in daily life. Pat is an AI conversational agent designed to augment a therapist-led PMT program by providing caregivers with real-time guidance, reinforcement, and answers to parenting questions between sessions.