JMIR Research Protocols

Zimbabwe currently faces a rapidly escalating burden of noncommunicable diseases (NCDs) concurrently with persistent communicable disease challenges, resulting in profound epidemiological differences between rural and urban populations. To effectively address this evolving epidemiological landscape and guide evidence-based public health interventions, reliable and high-quality longitudinal data are essential for capturing temporal shifts and contextual determinants often overlooked by conventional health information systems.

Cigarettes are a global public health concern, as cigarette smoking is the leading cause of death in the United States and throughout most high-income countries. Exposure to tobacco retail has been linked to adverse smoking outcomes, but research using naturalistic and causal approaches to quantify these effects in the real world remains relatively sparse. To address these gaps, this study used geolocation tracking, ecological momentary assessment, and neuroimaging to assess smoking outcomes in daily life and conducted a randomized controlled trial focused on the effects of exposure to tobacco retail.

Kidney transplant recipients present reduced physical function and a high prevalence of cardiometabolic complications, which increase cardiovascular risk and compromise long-term graft outcomes. Resistance training has demonstrated beneficial effects in this population; however, previous interventions have shown heterogeneity in load prescription and have not incorporated objective monitoring of movement velocity. Velocity-based resistance training (VBT) allows precise regulation of exercise intensity and fatigue, potentially improving the safety and individualization of exercise prescription in clinical populations.

Ovarian cancer (OC) is a highly fatal gynecologic malignancy with complex management challenges and limited long-term survival for advanced stages. Large language models (LLMs)—including systems such as GPT-4, Claude, Google Gemini, and others—are emerging artificial intelligence (AI) tools capable of performing health care–related tasks such as diagnostic support, treatment planning, report generation, and patient communication. However, their applications in OC care have not yet been comprehensively assessed.

Advances in antiretroviral therapy have transformed HIV into a chronic condition, leading to a growing population of adults aged 50 years and older living with HIV in Canada and globally. These individuals experience higher rates of multimorbidity, frailty, cognitive changes, and polypharmacy than their HIV-negative peers, and many rely on caregivers for emotional and practical support. Caregiving often occurs within chosen families of partners, friends, and community members, yet these caregivers remain largely unrecognized in policies that prioritize bio-legal family structures. Existing person- and family-centered care (PFCC) models in HIV focus mainly on pediatric and adolescent populations, leaving a critical gap in guidance for older adults and their diverse caregivers.

The global impact of sexually transmitted infections (STIs) significantly affects low- and middle-income countries (LMIC). Iin Kenya, where access to STI diagnostics is limited, effective diagnostic solutions are critically needed. Nucleic acid amplification tests are considered the laboratory gold standard for detecting pathogens such as and due to their high sensitivity and specificity. However, these methods typically require centralized laboratories, trained personnel, and longer turnaround times. FlashDx is a near–point-of-care molecular diagnostic platform designed to address these challenges by integrating automated sample processing and multiplex pathogen detection within a compact system suitable for decentralized use.

Women are largely underrepresented in clinical trials of antiretroviral therapy, constituting approximately 20% of participants. Trials often exclude pregnant women from participation to minimize fetal risk, and women enrolled in clinical trials who have a positive pregnancy test may be discontinued from the study. This results in an average 7-year lag between the time of drug approval and when dosing information for pregnancy is available. However, there may be low-risk opportunities to obtain dosing information earlier for pregnant women who need treatment with newer medications. We demonstrate this with a clinical trial of doravirine, a Food and Drug Administration (FDA)–approved nonnucleoside reverse transcriptase inhibitor, designed to assess the pharmacokinetics and dosing strategy for doravirine during pregnancy while maintaining safety for participants.

Children with cleft palate often experience impaired speech due to atypical velopharyngeal anatomy following palate repair surgery. While surgical repair aims to restore the function of the palate, more than one-third of cases result in continued velopharyngeal insufficiency (VPI) and require reoperation, which can result in negative psychosocial impacts and financial burden. Common surgical approaches for primary palatoplasty, including intravelar veloplasty and double-opposing Z-plasty, have similar reported rates of VPI. Furthermore, procedure selection occurs without any presurgical imaging and varies by operating surgeon. Surgical decisions are often based on intraoperative judgment rather than objective measures. Structural and functional variables have been associated with speech outcomes in this population, but these data exist independently of one another until the school-age years, failing to establish a direct connection between anatomy and functionality of the velopharynx in toddlers. The current clinical paradigm misses a critical window of opportunity for earlier diagnosis of VPI.

The introduction of pneumococcal vaccination programs in the United Kingdom has led to substantial reduction in the burden of pneumococcal disease in the general population, decreasing the incidence of invasive pneumococcal disease and preventing associated mortality. Current UK guidelines recommend pneumococcal vaccination for adults aged ≥65 years, as well as for individuals aged ≥2 years with underlying medical conditions that place them at increased risk of severe pneumococcal disease. This includes adults with immunosuppression. To date, there are few data in the United Kingdom of pneumococcal vaccine coverage in specific high-risk groups, such as those with immunocompromised conditions.

eHealth literacy is essential for enabling older adults to access, evaluate, and use digital health information effectively. Self-efficacy, defined as confidence in one’s ability to perform specific behaviors, is a key determinant of sustained health behavior change and has been widely applied in health education interventions. However, the effectiveness of self-efficacy–based interventions in improving eHealth literacy among community-dwelling older adults has not been systematically evaluated using high-quality evidence.

Demographic shifts are increasing the global demand for long-term care services, coinciding with a worldwide shortage of health care personnel. Service robots, designed to perform tasks in both professional and personal use, are perceived as a potential solution to alleviate health care personnel’s workload and enhance the quality of care. However, the existing literature is fragmented and heterogeneous, with a limited emphasis on the role of service robots in supporting residents rather than health care personnel. Furthermore, there is a lack of consistent definitions of service robotic technologies and a scarcity of studies on implementation models and frameworks.

Communication is a human right; despite this, children with cerebral palsy and dysarthria experience constraints due to their physical disability, affecting their opportunity to reach their full potential and ability to fully participate in play, school, and socializing. For many, their challenge to produce clear speech that can be understood is interpreted by others as an intellectual impairment. Children with moderate-severe speech impairments such as dysarthria often rely on technology solutions, which can translate text or symbols to speech manually or using a single binary switch, scanning hierarchical switch‑access menus. This is time-consuming, arduous, and 15 to 20 times slower than normal speech. Technology innovations, developed via high-quality datasets of audio-video samples of dysarthric speech, hold the key to build personalized speech-recognition algorithms that allow us to bridge this gap.