JMIR Formative Research

Digital interventions have been suggested to facilitate access to mental health care for refugees, who experience structural, linguistic, and cultural barriers to mental health care. Sleep-e, a digital sleep intervention originally developed for German teachers, has been culturally adapted for refugees in Germany mainly coming from African and Middle East countries. With the increasing number of refugees from Ukraine and the associated diversity of cultural backgrounds among refugees in Germany, it is essential to assess whether existing digital interventions are culturally appropriate for this target group as well.

Despite the availability of antiretroviral therapy (ART), only 66% of people with HIV in the United States achieve viral suppression, largely due to suboptimal ART adherence. Barriers such as limited access to care and forgetfulness impact adherence rates, which must be maintained at ≥95% to prevent viral load rebound. Combination interventions leveraging community health worker (CHW) support and mobile health (mHealth) technologies have the potential to overcome previously identified barriers and provide cost-effective support for improving adherence and viral suppression outcomes in people with HIV.

Despite the availability of prophylactic HPV vaccines, uptake remains suboptimal among young Black adults. Social media is a platform for the dissemination of health information and can be used to promote HPV vaccination among young Black adults.

Lyra Health’s short-term blended care therapy model, Lyra Care Therapy (LCT), has demonstrated effectiveness at scale. In LCT, clients participate in synchronous telehealth sessions and asynchronous guided practice sessions, in which they are provided with digital tools to reinforce key concepts and skills. These digital tools include animated video lessons that use storytelling to show characters learning and implementing new skills from therapy, written psychoeducational materials, interactive exercises that prompt reflection and skills practice, symptom assessments, and messaging with therapists. Past research on LCT found that time spent in therapy sessions and viewing digital video lessons predicts improvements in depression and anxiety symptoms.

Access to mental health services continues to pose a global challenge, with current services often unable to meet the growing demand. This has sparked interest in conversational artificial intelligence (AI) agents as potential solutions. Despite this, the development of a reliable virtual therapist remains challenging, and the feasibility of AI fulfilling this sensitive role is still uncertain. One promising approach involves using AI agents for psychological self-talk, particularly within virtual reality (VR) environments. Self-talk in VR allows externalizing self-conversation by enabling individuals to embody avatars representing themselves as both patient and counselor, thus enhancing cognitive flexibility and problem-solving abilities. However, participants sometimes experience difficulties progressing in sessions, which is where AI could offer guidance and support.

The use of virtual reality (VR) technology in rehabilitation therapy has been growing, leading to the development of VR-based upper-limb rehabilitation software. To ensure effective utilization of such software, usability evaluations are critical to enhance user satisfaction and identify potential usability issues.

In the modern economy, shift work is prevalent in numerous occupations. However, it often disrupts workers’ circadian rhythms and can result in shift work sleep disorder. Proper management of shift work sleep disorder involves comprehensive and patient-specific strategies, some of which are similar to cognitive behavioral therapy for insomnia.

Chemotherapy cycle prescription is generally carried out through a multi-step manual process which is prone to human errors. Clinical decision support tools can provide patient-specific assessments that support clinical decisions, improve prescribing practices, and reduce medication errors.

Family caregivers of individuals with dementia face significant mental health challenges. Acceptance and commitment therapy (ACT) has emerged as a promising intervention for improving these caregivers’ mental health. While various delivery modes of ACT have been explored, there is a need for evidence on the efficacy of videoconference-delivered ACT programs for this population.

Diabetic retinopathy (DR) is a leading cause of blindness globally. DR has increasingly affected both individuals and healthcare systems as the population ages.

The optimal response to a major incident in a road tunnel involves efficient decision-making among the responding emergency services (fire and rescue services, police, and ambulances). The infrequent occurrence of road tunnel incidents may entail unfamiliarity with the tunnel environment and lead to uncertain and inefficient decision-making among emergency services commanders. Ambulance commanders have requested tunnel-specific learning materials to improve their preparedness.

Systematic reviews are recognized as a high-level source of evidence in medical research but are often constrained by time-consuming manual screening of vast numbers of citations. The exponential growth of biomedical literature further complicates researchers’ ability to remain updated. Artificial intelligence (AI) may offer a solution, particularly through Large Language Models (LLMs), which excel at processing complex text. In this pilot study, we explored the feasibility of using five distinct LLMs to screen citations extracted from an existing systematic review on trauma hemorrhage. We selected ChatGPT 3.5, ChatGPT 4, Google Bard, Meta Llama 2 (70b parameters), and Claude AI 2, chosen for their widespread availability and text-comprehension abilities. In the original systematic review, 1,186 citations were screened by human reviewers, identifying 21 for full-text inclusion and excluding 1,165. From these excluded citations, we randomly sampled 100, yielding a total dataset of 121 records (21 included, 100 excluded). Each LLM received the original inclusion and exclusion criteria in a single-run format. We assessed performance by calculating sensitivity (correct identification of included abstracts), specificity (correct exclusion of irrelevant items), and overall accuracy. Sensitivity varied considerably, ranging from 57% (Claude2) to 100% (ChatGPT 3.5, Bard). Specificity was similarly diverse, from 18% (Llama2) to 79% (ChatGPT 3.5). ChatGPT 3.5 thus achieved the highest combined sensitivity and specificity. While these findings suggest that AI-driven LLMs could potentially streamline systematic review screening—perhaps even replacing a second human reviewer—significant limitations persist. Our pilot nature and single-run design curb generalizability, and some LLMs were prone to overinclusion. Larger, multi-run investigations are needed to assess probabilistic variability and refine prompt-engineering approaches. Nonetheless, this pilot evaluation highlights the emerging role of LLMs in citation screening and sets the stage for broader adoption, ensuring that ethical and procedural frameworks keep pace with AI’s rapid evolution.