Jeroan Julius Allison

Background: In Vietnam, an upper-middle-income country, the majority of people living with HIV smoke. Vietnam has established quitline services to support this population, but they remain underused. Text messaging is widely used in Vietnam, and studies in other countries have shown that text messaging interventions can promote smoking cessation. Our goal was to conduct a formative study to inform the development of a text messaging intervention to increase access to quitline services and promote smoking cessation among people living with HIV who smoke in Vietnam. Objective: This study aimed to identify factors influencing smoking behaviors among people living with HIV in Vietnam through qualitative interviews to inform a tailored text messaging intervention. Methods: In October 2022, study staff conducted 30- to 45-minute qualitative interviews with 20 people living with HIV from clinics in Hai Phong and Quang Ninh provinces, Vietnam. Interviews explored factors that influenced smoking, such as substance use, mental health, HIV stigma, social context, and health. A rapid qualitative analysis approach was used to synthesize themes from the data, including identifying key domains based on prior literature, developing a semistructured interview guide, and organizing data within domains to support iterative theme development. These interviews were used to develop prompts to guide people living with HIV who smoke in writing peer messages for a text messaging intervention. Results: Across 6 themes, participants described smoking as part of their everyday lives, shaped by social situations, stress, alcohol use, and their experiences living with HIV. Social smoking and early initiation were important influences on smoking behaviors, whereas financial concerns and protecting family members and others from secondhand smoke promoted cessation. Some participants used smoking to cope, whereas others wished to quit for better health. HIV stigma was a major contributor to smoking, with many participants describing increased smoking after diagnosis. Participants suggested that cessation support should be better tailored to their needs. Conclusions: Smoking behaviors among people living with HIV are shaped by overlapping influences, including societal norms, mental health, HIV-related stigma, and other structural stressors. An understanding of these overlapping factors can inform the development of text-based interventions that offer effective cessation support tailored to this population.

Introduction: Waterpipe tobacco smoking (WTP) is highly prevalent in Vietnam. WTP use is associated with higher nicotine exposure and increased risk of pulmonary and cardiovascular diseases. The aim of this study is to understand WTP use and how it may influence smoking behaviors and cessation...

Background: Community-based mobile health (mHealth) services are increasingly used to support chronic disease management in underserved rural populations facing workforce shortages, geographic isolation, and rapid aging. South Korea entered a super-aged society in December 2024, intensifying pressures in rural regions where multiple mHealth programs are embedded within primary care and public health systems. However, evidence on sustained use in real-world settings remains limited. Objective: This study aimed to explore user experiences and acceptance of community-based mHealth services in an underserved rural area of South Korea and identify facilitators and barriers to sustained engagement, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Methods: A convergent mixed methods design was used, with qualitative and quantitative data collected in parallel, analyzed separately, and integrated at the interpretation stage. Overall, 24 participants with ≥6 months of experience using 1 of 4 publicly funded mHealth services in Pyeongchang County, Gangwon State, were purposively recruited. Semistructured interviews guided by the UTAUT2 were analyzed using directed content analysis, combining deductive and inductive coding. Structured questionnaires assessing usability and behavioral intention were analyzed using descriptive statistics. Findings were integrated through joint interpretation. Results: Participants had a mean age of 71.3 (SD 9.2) years, and 70.8% (17/24) were female; hypertension (18/24, 75%) and hyperlipidemia (15/24, 58.3%) were the most common. Perceived difficulty was low (mean 2.54, SD 2.06, on a 0‐10 scale), intention for continued use was high (23/24, 95.8%), and recommendation intention was unanimous (24/24, 100%). Willingness to pay was reported by 79.2% (19/24), most commonly KRW 1000‐5000 (US $1-3) per month. Qualitative findings identified performance expectancy, social influence, facilitating conditions, and habit as the most salient determinants of sustained use. Real-time monitoring enhanced health awareness, motivated dietary modification, and increased physical activity. Public health center nurses served as human-in-the-loop facilitators, providing continuous training, troubleshooting, and emotional support, while family and peers reinforced engagement. Habit formation emerged as a central mechanism, with 91.7% (22/24) integrating mHealth use into routines anchored to waking, exercise, and bedtime. Effort expectancy barriers among older participants were mitigated through nurse-led training, and hedonic motivation was driven by intrinsic satisfaction and peer interaction. Integrated analysis showed convergence for ease of use and behavioral intention, and partial divergence for willingness to pay. Conclusions: Community-based mHealth services were successfully integrated into daily life and supported chronic disease self-management among older adults in an underserved rural setting. Sustained engagement was driven by perceived health benefits, continuous human support, and habit formation rather than technology features alone, underscoring the importance of relationship-centered, human-in-the-loop implementation models. Strengthening intuitive design, hands-on onboarding, multidisciplinary primary care teams, and stable financing will be essential for equitable digital health adoption in rural and aging communities.

Background: Trustworthy artificial intelligence (AI) in health care requires assurance frameworks that translate ethical principles into measurable governance and evaluation practices. While a growing number of AI assurance frameworks have been proposed, they differ substantially in governance structure, institutional embedding, and implementation mechanisms, reflecting differences in intended purpose and use. To date, few studies have applied standardized, rubric-based evaluation criteria to systematically compare how assurance instruments with different institutional origins operationalize ethical principles across the AI lifecycle. Objective: This study aimed to develop and apply a structured, rubric-based evaluation instrument to compare 2 health care AI assurance instruments, including the Coalition for Health AI (CHAI) responsible AI guide, a voluntary consortium-based instrument, and South Korea’s Trustworthy AI guideline, a government-issued instrument. Methods: A 7-dimension evaluation rubric was developed based on a synthesis of established international AI assurance and governance instruments. The rubric covered core principles, AI lifecycle coverage, governance context, stakeholder breadth, operational maturity, instrument design and tools, and public accessibility. Seven independent evaluators with expertise in health care AI governance assessed each instrument using a 5-point ordinal rating scale (1=absent-5=comprehensive). Each evaluator independently scored the materials using a standardized rubric. Discrepancies were resolved through structured consensus discussions, with reference to rubric definitions and source documents. Final scores were determined based on documented evidence, requiring full consensus rather than averaging. Interrater reliability was assessed using Fleiss kappa. Results: Both instruments demonstrated strong alignment in core principles (CHAI: 4; Trustworthy AI Guideline: 5) and stakeholder breadth (both: 4). The government-issued Trustworthy AI Guideline exhibited broader AI lifecycle coverage (5 vs 4), a more formalized governance context (5 vs 3), and higher operational maturity (4 vs 2), reflecting stepwise oversight and formal embedded oversight mechanisms supported by legislation. In contrast, the voluntary CHAI instrument demonstrated greater emphasis on instrument design and implementation tools (4 vs 3) and higher public accessibility (5 vs 3), driven by open-access resources such as assurance standards guides and applied model cards. Interrater agreement of independent ratings was moderate to substantial (Fleiss kappa=0.47-0.64; P <.001), indicating consistent scoring patterns among evaluators. Conclusions: This comparative analysis indicates that voluntary and government-issued AI assurance instruments operationalize trustworthy AI principles in distinct but complementary ways. Voluntary instruments emphasize flexible tools and accessible implementation resources, while government-issued guidelines embed assurance functions within formal governance and oversight structures. Rather than representing competing models, these approaches address different assurance needs across the AI lifecycle. By identifying concrete areas of alignment and divergence, this study supports a more coherent comparison of assurance practices and highlights potential opportunities for alignment across documentation structures and evaluation approaches that can support safe, equitable, and scalable deployment of health care AI across diverse institutional contexts.

Artificial intelligence (AI) has emerged as a powerful tool for fostering positive behavior change and enhancing mental health support. However, the abrupt discontinuation or functional degradation of AI-driven interventions, particularly those featuring conversational agents, may trigger unintended psychological consequences. Therefore, we introduce and examine the concept of Artificial Intelligence Discontinuation Effects (AI-DICE), drawing parallels from abandonment-like experiences observed from problematic termination experiences with therapists. We propose a conceptual framework for AI-DICE mitigation that draws on evidence-based behavior change principles and explores clinical modalities that may inform mitigation toolkits (eg, Acceptance and Commitment Therapy, Cognitive Behavioral Therapy, Dialectical Behavior Therapy, and Motivational Interviewing). We also ground our approach in user experience research and community-engaged research. AI-DICE raises critical ethical challenges, including transparency, the ability to withdraw or adapt participation as the users’ knowledge of the intervention grows, and access to support postintervention. Prioritizing long-term continuation, or at least some form of ongoing access, over the best-planned complete discontinuation strategy may help ensure that AI-driven mental health solutions deliver lasting benefits rather than unintended harm. Finally, although not yet empirically established, incorporating discontinuation planning and AI-DICE mitigation from the outset may also improve intervention effectiveness by strengthening user autonomy, supporting skills transfer, and reducing dependence-related vulnerabilities.

Background: Computed tomography pulmonary angiography (CTPA) is the standard imaging modality for diagnosing pulmonary embolism (PE), but diagnostic uncertainty is common due to technical limitations and vague language, leading to inconsistent interpretation and clinician frustration. Objective: This study develops a prompt-free, data-efficient method for assessing diagnostic certainty of PE in CTPA reports using small pretrained language models. Methods: This study examined 173 consecutive CTPA reports from UMass Memorial Health, each annotated by 3 radiologists for PE diagnostic certainty. We developed PECertainty, a lightweight, prompt-free model, and compared it with advanced large language model (LLM)–based methods under limited supervision settings. Baselines included prompt-free methods (support vector machine, random forest, and RoBERTa [Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach]) and prompt-dependent methods (LLM fine-tuning, in-context learning, and ADAPET [A Densely-Supervised Approach to Pattern Exploiting Training]; UNC Chapel Hill) with open-source Gemma3-4B (Google DeepMind) and Llama3.2-3B (Meta), and the proprietary GPT-3.5 (OpenAI). Sensitivity analyses evaluated performance with 1 to 10 training examples per category for the top performer. Model performance was evaluated against radiologist annotations. External validation on 420 CTPA reports from the Baystate Medical Center, with validation limited to distinguishing certain from uncertain reports. Interpretability of the top-performing models (PECertainty and GPT-3.5) was evaluated using integrated gradients and prompt-based explanations reviewed by radiologists. Results: Among prompt-dependent methods, GPT-3.5 fine-tuning ( F ₁ -score 0.86; 95% CI 0.71‐1.0) and in-context learning ( F ₁ -score 0.87; 95% CI 0.71‐1.0) performed best, and the performance of in-context learning consistently outperformed 0-shot learning for Gemma3-4B ( F ₁ -score 0.63, 95% CI 0.56‐0.79 vs F ₁ -score 0.45; 95% CI 0.29‐0.56) and Llama3.2-3B ( F ₁ -score 0.54; 95% CI 0.41‐0.71 vs F ₁ -score 0.43, 95% CI 0.28‐0.62). PECertainty demonstrated numerically better or equivalent performance compared with both the top-performing prompt-dependent methods and all prompt-free baselines . Compared with fine-tuned ClinicalBERT (Bidirectional Encoder Representations From Transformers Pretrained on Clinical Text), PECertainty achieved statistically significant improvements across all metrics (paired bootstrap significance test, P <.05). RoBERTa (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach) fine-tuning lagged ( F ₁ -score 0.52; 95% CI 0.35‐0.71), and simple models such as support vector machine underperformed. In few-shot settings (10 examples/category), PECertainty ( F ₁ -score 0.80; 95% CI 0.59‐0.94) outperformed both GPT-3.5 fine-tuning ( F ₁ -score 0.74; 95% CI 0.58‐0.88) and in-context learning ( F ₁ -score 0.65; 95% CI 0.47‐0.83). External validation on the Baystate dataset showed good generalization for distinguishing certain from uncertain cases ( F ₁ -score 0.77; 95% CI 0.70‐0.83). Despite its strong performance, PECertainty was rated as less interpretable than fine-tuned GPT-3.5 by radiologists ( t test, P <.05). Conclusions: PECertainty enables accurate and data-efficient assessment of diagnostic certainty from free-text CTPA reports in low-resource settings. As an open-source, lightweight alternative to proprietary LLMs, it may support more precise communication between radiologists and referring physicians, with interpretability identified as a key direction for improvement.

Introduction: Limited research has been conducted on the experiences of ART adherence among young HIV-positive men who have sex with men (MSM) and transgender women (TGW) in Zambia. This study aimed to address this evidence gap through elucidating the manifestations of stigmas intersecting with antiretroviral adherence in young key populations living with HIV in Zambia. Methods: This study used a mixed-methods design. In-depth interviews collected qualitative data, and a questionnaire collected quantitative data. We used thematic analysis for qualitative data. Qualitative data were descriptively analysed using Stata. ART adherence was defined as ever missing an ART dose (yes/no). Frequencies and percentages were generated for ART adherence by socio- demographic characteristics and stigma indicators. Results: We recruited 56 participants from three sites: Lusaka, Chipata and Solwezi districts. Participants’ mean age was 23 years. Half of the participants reported ever missing an ART dose, with equal proportions among MSM (50%) and TGW (50%). Missed doses were more frequent among participants who experienced HIV related stigma (59.4%) compared with those who did not (37.5%). Missed doses were more prevalent among those living with family (88.9%) compared to those not living with family. Stigma related to sexual orientation or gender identity (SOGI) showed mixed patterns, with no consistent differences in missed doses. Variations in adherence were observed across age, education, employment, living arrangements and care settings, however, there were no clear patterns that had emerged due to the small sample. The emergent themes were sexual orientation and gender identity–based stigma, negotiating self-identity amid uncertainty, stigma experiences in the family and ART adherence, sexual orientation, HIV ART adherence and disclosure challenges, as well as restarting ART. Conclusion: The study provides evidence of how stigmas intersect with antiretroviral therapy (ART) among young key populations. Meanwhile, most of the stigma that affected adherence to ART was related to HIV. Supporting adherence to ART among adolescents and young people, including sexual minority groups, may require tailored approaches that address their lived experiences.

BACKGROUND Chronic disease management requires sustained engagement, trust, and collaboration among patients, families, and primary care teams. In underserved rural areas with aging populations, mobile health (mHealth) services offer opportunities to support remote monitoring and self-management; however, real-world user experiences and determinants of sustained adoption remain underexplored. OBJECTIVE This study explored the lived experiences and acceptance of community-based mHealth services among residents of an underserved rural area in South Korea, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. METHODS A convergent mixed-methods design was employed. Twenty-four participants using community-based mHealth services were recruited through public health centers. In-depth semi-structured interviews were analyzed using directed content analysis guided by UTAUT2 constructs, with inductive coding for emergent themes. Quantitative survey data on usability, service use, and willingness to pay were analyzed descriptively to complement qualitative findings. RESULTS Participants were predominantly older adults (mean age 71.3 years) with multimorbidity, most commonly hypertension, hyperlipidemia, and diabetes. Performance expectancy, social influence, facilitating conditions, and habit emerged as the strongest drivers of acceptance and sustained use. Real-time visualization of health data motivated lifestyle changes and reinforced perceived health benefits. Nurse-involved, human-in-the-loop support-including troubleshooting, interpretation of data, and group-based activities-was critical in overcoming early usability challenges and fostering trust. Habit formation was evidenced by the integration of monitoring activities into daily routines. Perceived ease of use varied by age and prior digital experience, with older participants requiring longer adaptation periods. Nineteen participants expressed willingness to pay for continued access, whereas others cited sufficient self-management confidence or device-related issues as reasons for discontinuation. CONCLUSIONS Community-based mHealth services embedded within nurse-involved and relationship-centered care models can be successfully integrated into daily life among rural older adults. Sustained adoption depends not only on technology usability but also on continuous human support, social engagement, and habit formation. Digital health strategies for underserved rural communities should prioritize intuitive design, structured onboarding, and long-term investment in primary care teams to promote equity, sustainability, and effective chronic disease self-management.