JMIR Formative Research

Software solutions for wearable-based stress monitoring offer significant potential in healthcare, particularly for vulnerable populations such as individuals with dementia or persistent physical symptoms. Despite technological advances, designing user-centered, ethically grounded, and contextually relevant software remains challenging. Vulnerable populations often have specific cognitive, physical, and emotional needs that require customization, yet these are rarely prioritized in mainstream development. Our so-called Sensors2Care project addressed these challenges by co-developing stress-monitoring prototypes in collaboration with stakeholders from healthcare, law, and technology within a transdisciplinary setting.

Cardiac arrest (CA), characterized by an extremely high mortality rate, remains one of the most pressing global public health challenges. It not only causes a substantial strain on health care systems but also severely impacts individual health outcomes. Clinical evidence demonstrates that early identification of CA significantly reduced the mortality rate. However, the developed CA prediction models exhibit limitations such as low sensitivity and high false alarm rates. Moreover, issues with model generalization remain insufficiently addressed.

Access to care that affirms their entire self is essential, especially for gender-diverse individuals. Gender-affirming care includes medical, social, and non-medical supports to affirm gender identity.

A growing volume of mental health research is conducted with participants recruited and responding online. However, to date, few psychometric scales have been specifically validated for online research.

Digital wound monitoring has become increasingly feasible with the widespread use of smartphones and mobile messaging platforms. Although most previous studies have focused on chronic wounds and demonstrated the clinical benefits of remote monitoring, little is known about how patients with acute wounds perceive and report wound-related changes after discharge; these factors may affect the accuracy and reliability of patient-facing digital health systems.

Adolescent obesity remains a pressing public health challenge, particularly among socioeconomically disadvantaged populations. Artificial intelligence (AI) holds the promise for supporting students in managing daily health behaviors, but few existing studies employed AI-based interventions in naturalistic settings such as schools.

Complimentary subscriptions to UpToDate, a decision support tool, were provided to community health workers (CHWs) in rural and remote primary care sites as part of a government-funded health system research program. A feasibility evaluation conducted after the first year of implementation showed that UpToDate was acceptable among CHWs despite infrastructural barriers.

Bipolar disorder requires immediate and frequent daily symptom monitoring due to its extreme mood fluctuations. Ecological momentary assessment (EMA) technology uses high-frequency data collection to achieve ecologically valid capture of patient symptoms. Investigating EMA compliance among Chinese patients with bipolar disorder and its influencing factors is essential for developing more feasible daily symptom monitoring protocols.

Digital vaccination campaigns are increasingly used to address declining vaccine confidence, yet evidence from large-scale, real-world interventions in middle-income countries is limited. Meta’s Brand Lift Studies (BLS), which use randomized test–control exposure, provide Bayesian esti-mates of attitudinal shifts resulting from digital content. Mexico, with over 88.6 million active internet users, provides a setting to evaluate the impact of targeted campaigns on vaccine atti-tudes.

Biomedical research studies are increasingly using digital to enroll, recruit and collect data from participants. However, variability in digital literacy and technological acceptance can be challenging for recruitment from groups traditionally underrepresented in research, including those served by Federally Qualified Health Centers.

Although sexual exploration is normative during adolescence, sexual activities that are unprotected and occur under the influence of substances can pose significant risks to young people. Youth exposed to adversity are among the groups most vulnerable to sexual risk-taking in adolescence. Selective interventions that consider lived experiences and the local context may help reduce sexual risk-taking among this population.

This study demonstrates that GPT-4o outperforms traditional natural language processing methods in accurately analyzing patient sentiment toward atopic dermatitis treatments on Reddit, enabling more nuanced and reliable extraction of real-world patient perspectives from large-scale social media data.