JMIR Research Protocols

HIV continues to disproportionately impact sexual and gender minority youth (SGMY) in the United States. Public health efforts have increasingly focused on developing efficacious interventions to curb the spread of HIV among SGMY and help those living with HIV achieve and sustain viral suppression. However, recruiting and engaging SGMY in prevention and care interventions is challenging.

Tuberculosis (TB) is an urgent global health threat and the world’s deadliest infectious disease despite being largely curable. A critical challenge is to ensure that patients adhere to the full course of treatment to prevent the continued spread of the disease and development of drug-resistant disease. Mobile health interventions hold promise to provide the required adherence support to improve TB treatment outcomes.

Assessment of the subjective experiences of individuals with maxillary anterior (ie, the upper front region of the mouth) single-tooth implants is limited mainly to quantitative measurements of satisfaction with appearance. Interestingly, there is unexplained variability in the relationship between satisfaction and appearance.

Integrated prevention at work promises to eliminate the boundaries between primary, secondary, and tertiary prevention actions taken by stakeholders in the world of work. It is receiving increasing attention from the scientific community because of its concerted and harmonized approach, which promotes employment access, return, and healthy long-term continuation. Although promising, integrated prevention is not yet well-defined, which makes it difficult to operationalize.

Major depressive disorder (MDD) is a prevalent and debilitating mental health disorder. Among different therapeutic approaches (eg, medication and psychotherapy), psychotherapy in the form of cognitive behavioral therapy (CBT) is considered the gold standard treatment for MDD. However, although efficacious, CBT is not readily accessible to many patients in need because of hurdles such as stigma, long wait times, high cost, the large time commitment for health care providers, and cultural or geographic barriers. Electronically delivered cognitive behavioral therapy (e-CBT) can effectively address many of these accessibility barriers.

A major challenge for prospective, clinical tuberculosis (TB) research is accurately defining a metric for measuring medication adherence.

A systematic review can be defined as a summary of the evidence found in the literature via a systematic search in the available scientific databases. One of the steps involved is article selection, which is typically a laborious task. Machine learning and artificial intelligence can be important tools in automating this step, thus aiding researchers.

There is a growing global need for scalable approaches to training and supervising primary care workers (PCWs) to deliver mental health services. Over the past decade, the World Health Organization Mental Health Gap Action Programme Intervention Guide (mhGAP-IG) and associated training and implementation guidance have been disseminated to more than 100 countries. On the basis of the opportunities provided by mobile technology, an updated electronic Mental Health Gap Action Programme Intervention Guide (e-mhGAP-IG) is now being developed along with a clinical dashboard and guidance for the use of mobile technology in supervision.

More than 88 million Americans are at risk of developing type 2 diabetes mellitus (T2DM). The National Diabetes Prevention Program’s Lifestyle Change Program (DPP LCP) has been shown to be effective in reducing the risk of progressing from prediabetes to T2DM. However, most individuals who could benefit from the program do not enroll.

Telehealth videoconferencing has largely been embraced by health care providers and patients during the COVID-19 pandemic; however, little is known about specific techniques for building rapport and provider-patient relationships in this care environment. Although research suggests that videoconferencing is feasible and can be effective for some types of care, concerns about the impact of technology on provider-patient relationships exist across health disciplines. Suggestions for adapting some in-person rapport techniques, such as the use of small talk, eye contact, and body language to facilitate trust, personal connection, and communication during videoconferencing encounters, have been discussed in the popular press and clinical commentaries. Notably, evidence regarding the effects of these strategies on rapport and clinical care outcomes is lacking. Understanding how to establish rapport in videoconferencing visits is especially important in oncology nursing, where rapport with patients enables nurses to become a source of emotional support, helping patients adapt and navigate the cancer journey.

Australia’s mental health care system has long been fragmented and under-resourced, with services falling well short of demand. In response, the World Economic Forum has recently called for the rapid deployment of smarter, digitally enhanced health services to facilitate effective care coordination and address issues of demand. The University of Sydney’s Brain and Mind Centre (BMC) has developed an innovative digital health solution that incorporates 2 components: a highly personalized and measurement-based (data-driven) model of youth mental health care and a health information technology (HIT) registered on the Australian Register of Therapeutic Goods. Importantly, research into implementation of such solutions considers education and training of clinicians to be essential to adoption and optimization of use in standard clinical practice. The BMC’s Youth Mental Health and Technology Program has subsequently developed a comprehensive education and training program to accompany implementation of the digital health solution.

Diagnostic decision making, especially in emergency departments, is a highly complex cognitive process that involves uncertainty and susceptibility to errors. A combination of factors, including patient factors (eg, history, behaviors, complexity, and comorbidity), provider-care team factors (eg, cognitive load and information gathering and synthesis), and system factors (eg, health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Using electronic triggers to identify records of patients with certain patterns of care, such as escalation of care, has been useful to screen for diagnostic errors. Once errors are identified, sophisticated data analytics and machine learning techniques can be applied to existing electronic health record (EHR) data sets to shed light on potential risk factors influencing diagnostic decision making.