Tag: pubmed

AIDS Educ Prev. 2026 Aug;38(4):307-323. doi: 10.1521/aeap.2026.38.4.307.

ABSTRACT

To characterize the lived experience of persons living with HIV/AIDS (PLWHA) during the COVID-19 pandemic, we utilized an innovative survey to assess the experiences of 94 adult respondents receiving medical care and/or case management services from two Ryan White funded sites in New Brunswick, NJ, from May 2020 to November 2021. The Local Inventory of Needs and Knowledge-HIV (LINK-HIV) survey includes five indices and assesses overall and mental/emotional health of PLWHA. We demonstrate internal validity of the indices, describe relationships between indices and health outcomes, and characterize the health of this population. We identified specific unmet needs across the constructs of social determinants of health (SDOH) Needs, Stress due to unmet SDOH, Access to Healthcare, Stress due to COVID-19, and Person-Centered Primary Care. Respondents’ self-reported health was suboptimal (47.8% reported better overall health, 40.4% reported better mental/emotional health), and associations were seen between responses to the five indices and self-reported health outcomes.

PMID:42574704 | DOI:10.1521/aeap.2026.38.4.307

AIDS Educ Prev. 2026 Aug;38(4):284-306. doi: 10.1521/aeap.2026.38.4.284.

ABSTRACT

HIV status disclosure is an ethical obligation and, in some jurisdictions, a legal requirement that supports treatment adherence. In Eastern and Southern Africa (ESA), where intimate partner violence (IPV) is prevalent, disclosure may have harmful consequences. The objective of this scoping review was to map the relationship between IPV and HIV disclosure across health care settings in ESA. Following PRISMA-ScR guidelines, peer-reviewed English-language studies (2012-2024) were identified through EBSCOhost, PubMed, and Google Scholar using a SPICE-informed search strategy. Thirty-six quantitative, qualitative, and mixed-methods studies met the inclusion criteria and were analyzed thematically. Three themes emerged: factors influencing disclosure, including relationship dynamics and fear of violence; the positive and negative consequences of disclosure, particularly IPV; and the role of health care workers, whose limited IPV training tended to increase risk. Integrating IPV screening, safety planning, and gender-sensitive training into HIV counseling is essential to support safe, client-led disclosure.

PMID:42574702 | DOI:10.1521/aeap.2026.38.4.284

JMIR Mhealth Uhealth. 2026 Aug 10;14:e94793. doi: 10.2196/94793.

ABSTRACT

BACKGROUND: Cancer affects multiple physical, psychological, and social aspects of an individual’s life. Cancer survivors frequently report unmet needs long after diagnosis and require ongoing support. AI is increasingly embedded in patient-facing digital health technologies (DHTs) in oncology, yet its impact on different domains of patients’ and survivors’ health-related quality of life (HRQOL) remains unclear.

OBJECTIVE: This systematic review aims to (1) examine how AI has been integrated into patient-facing DHTs designed to support cancer survivors, (2) narratively synthesize the potential effects of these technologies on HRQOL and provide preliminary quantitative estimates through an exploratory meta-analysis, and (3) explore broader changes in additional patient-reported outcomes (PROs; secondary aim).

METHODS: PubMed, PsycINFO, Embase, Scopus, CINAHL, and the Cochrane Library were searched for articles published between January 2020 and August 2025. Reference lists of included articles were hand-searched for additional eligible studies. Eligible studies enrolled cancer survivors of any age and disease stage, evaluated a patient-facing DHT with AI components, and assessed HRQOL. Nonoriginal research and non-English reports were excluded. Risk of bias was assessed in all controlled studies using RoB 2 (revised Cochrane risk of bias 2) or ROBINS-I V2 (Risk of Bias in Non-Randomized Studies-of Interventions, Version 2), according to study design. Data on HRQOL and other PROs were synthesized narratively, and exploratory random-effects meta-analyses were conducted for HRQOL domains.

RESULTS: Eight reports from 7 studies from China and the United States (N=2867 participants) met the inclusion criteria, and 3 (n=292 participants) contributed to the exploratory meta-analysis. All studies included adults with various cancers at different stages and times since diagnosis. Most studies showed low risk of bias or some concerns (RoB 2), but one was evaluated as having a serious risk of bias (ROBINS-I V2). AI applications ranged from symptom monitoring to targeted education. The narrative synthesis suggested positive effects on selected HRQOL domains, particularly general health, with more pronounced effects in studies conducted in China. Exploratory meta-analyses demonstrated provisional moderate positive effects on global health (Hedges g=0.77, 95% CI 0.15-1.40) and social functioning (Hedges g=0.75, 95% CI 0.08-1.42), but no effects on physical functioning, role functioning, or emotional well-being. Other PROs indicated generally high user satisfaction and adherence, improved mental health outcomes, and reductions in physical symptoms. Only minor and mild adverse events were reported.

CONCLUSIONS: Current evidence, although limited, suggests that AI-enabled patient-facing DHTs may benefit survivors’ HRQOL and other PROs, particularly in early survivorship. However, our findings are based on small and heterogeneous studies and should therefore be interpreted with caution. Robust trials with adequate sample sizes, longer follow-up, and appropriate control conditions, including DHTs without AI components, are needed to determine the specific contribution of AI.

PMID:42574700 | DOI:10.2196/94793

JMIR Med Inform. 2026 Aug 10;14:e93680. doi: 10.2196/93680.

ABSTRACT

BACKGROUND: The medical burden caused by stroke is increasingly severe, and a small minority of high-cost patients consume the majority of medical expenditures. Therefore, revealing the formation mechanisms of this population and exploring a scientific cost-risk stratification system are crucial for improving the quality of care and achieving the optimal allocation of medical resources.

OBJECTIVE: This study aimed to construct a comorbidity network for patients with stroke using standardized front-page medical record data, extract network features that reflect complex disease interactions, and develop identification models in combination with machine learning algorithms. The study focused on building a core model integrating variables from the near-discharge stage for stratifying the risk of high hospitalization costs in patients at the near-discharge stage. In addition, an early prediction model was developed using only data available at admission.

METHODS: We conducted a retrospective study, collecting the hospital discharge data of inpatients with stroke from a tertiary hospital in Northeast China between 2021 and 2023. The data from 2021 to 2022 were used to construct a network and extract features to capture the potential relationship between diseases and high costs. Using the 2023 data partitioned into training and testing sets, we developed 5 models to identify inpatients with stroke who incurred high hospitalization costs and compared their performance when input with different features. In addition, the Shapley Additive Explanations interpretability method was adopted to explain the global and local contributions of the model features.

RESULTS: The inclusion of network features significantly improved the model’s performance, among which Extreme Gradient Boosting performed the best. The global feature importance showed that network features occupied a major proportion. The results of the Shapley Additive Explanations interaction analysis indicated potential phased changes in patient resource consumption. However, the overall performance of the early identification model constructed solely from admission data was subject to clear limitations.

CONCLUSIONS: This study developed an integrated framework combining comorbidity network analysis with machine learning, which significantly improved the accuracy of identifying inpatients with stroke at high risk of incurring excessive hospitalization costs. The core model demonstrated good performance in risk stratification during the near-discharge stage, showing potential for application in the formulation of risk management strategies and the optimization of health care resource allocation. It also laid the foundation for the subsequent development of more accurate early identification models.

PMID:42574699 | DOI:10.2196/93680

Nurs Open. 2026 Aug;13(8):e70743. doi: 10.1002/nop2.70743.

ABSTRACT

AIM/OBJECTIVE: To evaluate health science students’ learning experience and perceptions of GENIE (Gamified Asynchronous LEarNing of IPE Telehealth) and gather feedback to refine the module’s design and improve learners’ engagement.

BACKGROUND: Online telehealth education helps health science students build professional knowledge while offering flexibility in meeting clinical hour requirements. To make this learning more engaging, we developed GENIE, a gamified module based on Keller’s ARCS motivational framework. While gamification can enhance learning, how students actually interact with GENIE’s features remains unclear.

DESIGN: An exploratory mixed-methods, cross-sectional study was conducted among health science students at the University of Utah Health campus using convenience sampling. Participants were voluntarily recruited through the Interprofessional Education (IPE) program between October and December 2022.

METHODS: We conducted an exploratory mixed-methods, cross-sectional study with 30 health science students. Quantitative data were collected using the Game Experience Questionnaire (GEQ), Post-Game Experience Questionnaire (PGQ) and the Situational Motivation Scale (SIMS). Qualitative data were obtained through open-ended questions administered via REDCap.

RESULTS: Participants were primarily female nursing students (53%, n = 16). The quantitative and qualitative findings were consistent, indicating that students perceived GENIE as moderately effective in its use of gamification. GEQ results indicated low to moderate engagement, while SIMS scores reflected moderately positive motivation. PGQ findings suggest that the GENIE module was manageable and elicited neutral emotional engagement. Open-ended feedback emphasized a desire for more challenging game elements and richer interactive storytelling features.

CONCLUSIONS: This exploratory evaluation suggests that gamified online telehealth learning may support health science students’ engagement and motivation in online telehealth courses. Participants identified more challenging elements, narrative-driven interactions and interactive features as key opportunities to enhance the GENIE module. These preliminary findings can inform the future development and evaluation of gamified approaches in health science education.

IMPLICATIONS TO NURSING PRACTICE: The findings support incorporating gamification into nursing and health science education to enhance student motivation, engagement and teamwork. Gamified telehealth learning experiences may better prepare students for telehealth communication and interprofessional collaboration in future virtual care practice.

PMID:42574683 | DOI:10.1002/nop2.70743

Nurs Open. 2026 Aug;13(8):e70735. doi: 10.1002/nop2.70735.

ABSTRACT

AIM: This study aims to examine the relationship between microplastic pollution awareness and environmental literacy levels among undergraduate nursing students.

DESIGN: The study was conducted using a cross-sectional design.

METHODS: This study included 356 undergraduate nursing students selected from each academic year using proportional stratified sampling. Data were collected between March and June 2023 using a demographic form, the Microplastic Pollution Awareness Scale, and the Environmental Literacy Scale. Data analysis was performed using SPSS 23.0, with descriptive statistics, Mann-Whitney U, Kruskal-Wallis, and Spearman correlation analyses.

RESULTS: The mean microplastic pollution awareness score and environmental literacy score were 22.98 ± 4.85 and 85.21 ± 10.35, respectively. Participants who expressed willingness to receive environmental education and those who had previously heard of microplastics demonstrated higher mean scores for both microplastic pollution awareness and environmental literacy. A considerable proportion of participants reported not having previously heard of microplastics. Despite this, most nursing students (94.4%) exhibited high levels of environmental literacy. A positive correlation was found between microplastic pollution awareness and environmental literacy.

CONCLUSION: This study highlights the potential importance of environmental literacy in raising nursing students’ awareness of microplastic pollution. The current curriculum has limited content on environmental health, and it is mostly covered in a short section of the public health nursing course. These findings suggest that integrating content on environmental literacy and microplastic pollution across all stages of the nursing curriculum, as well as in in-service training, may be beneficial.

IMPLICATIONS FOR NURSING PRACTICE: Strengthening environmental literacy in nursing education may improve awareness of microplastic pollution and support nurses in promoting environmentally responsible behaviours and environmental health in clinical and community settings.

IMPACT: This study addresses the limited evidence on microplastic pollution awareness among nursing students. The findings highlight the importance of environmental literacy in shaping awareness and can inform curriculum development and educational strategies targeting future nurses.

REPORTING METHOD: This study was reported in accordance with the STROBE guidelines.

PATIENT OR PUBLIC CONTRIBUTION: No patient or public involvement.

PMID:42574677 | DOI:10.1002/nop2.70735

JMIR Mhealth Uhealth. 2026 Aug 10;14:e85197. doi: 10.2196/85197.

ABSTRACT

BACKGROUND: Advances in sensor technologies and increased adoption of wearables and smartphones by individuals have led to an abundance of patient-generated health data (PGHD). This data, when used effectively, could help to further augment the process of shared decision-making (SDM) to enable patient-centered care. However, the possible integration and usage of PGHD introduces complexities and challenges, which warrant considering both health care professional (HCP) and patient perspectives.

OBJECTIVE: Summarize the relevant works from the past 10 years that reflect the perspectives of both HCPs and patients as key stakeholders on potential barriers and enablers to the integration of PGHD for SDM. We analyzed both perspectives to surface key challenges and opportunities with integrating PGHD throughout patient journeys, as well as clinical workflows.

METHODS: Electronic searches were done using 3 databases: PubMed, ACM Digital Library, and IEEE Xplore for papers published between March 2013 and March 2023. Enablers and barriers mentioned by the stakeholders in included papers were extracted and analyzed using thematic analysis. An existing six-stage workflow model for integrating PGHD was used as a reference for deductive coding. Subsequently, considering barriers and enablers faced by both the HCPs and patients uncovered various tensions and alignments of perspectives, which could be addressed in future work and can inform concepts, designs, and development in PGHD for SDM.

RESULTS: A total of 53 publications were included in the scoping review. Six main overarching themes for barriers and enablers were identified: (1) patient-provider relationship, (2) patient characteristics, (3) organizational factors, (4) medical ethics and law, (5) data-driven workflow, and (6) design and technology. The 6-stage workflow was further expanded based on the new findings to include 4 additional stages, which include contextual considerations outside of traditional clinical environments. In addition to partially corroborating previously established barriers in the 6-stage workflow model, several new barriers and enablers were identified throughout all stages. This model helps to further align the needs of HCPs and patients beyond the clinical setting and could benefit system designers who plan to integrate PGHD for SDM.

CONCLUSIONS: This scoping review demonstrates that there are several factors to consider for effectively integrating PGHD into health-related SDM. Notably, such factors extend outside the boundaries of traditional clinical settings. Although there is agreement between HCPs and patients on certain factors, there are also tensions to be addressed. Our augmented 10-stage workflow model offers system designers an overview of the challenges and enablers to consider while designing for PGHD integration in clinical workflows and patient journeys to improve SDM.

PMID:42574670 | DOI:10.2196/85197

JMIR Ment Health. 2026 Aug 10;13:e94753. doi: 10.2196/94753.

ABSTRACT

BACKGROUND: Two-fold increases in the prevalence of youth anxiety and depression over the last two decades have mirrored exponential growth in opportunities for adolescent online social interaction via social media, short messaging service (SMS), and internet text messaging apps on smartphones. However, studies to date of self-reported online social interaction time have produced conflicting results. Understanding the role of dispositional and developmental differences in individuals’ responses to online versus offline social interactions may help elucidate whether and how online social interaction is related to anxiety and depression.

OBJECTIVE: This study aimed to investigate the relationship between older adolescents’ and emerging adults’ (18-24-year-olds) mental health and (1) objectively measured time spent on smartphones and online social interaction apps, (2) momentary affective and affiliative responses to online and offline social interactions, and (3) the moderating role of developmentally and dispositionally elevated social sensitivity.

METHODS: Smartphone, social media (eg, Instagram), SMS, and internet (eg, WhatsApp) text messaging app time from participants’ screen use settings, as well as symptoms of anxiety and depression, and social sensitivity, were measured in 190 older adolescents and emerging adults (mean age 20.4, SD 2.2 years). Participants then completed a novel ecological momentary assessment (EMA) capturing affective and affiliative responses to recent online or offline social interactions 3× daily for 1 week. Symptoms of mental health were assessed again after 1 month.

RESULTS: Total online social interaction (combined social media and text messaging) app time, but not total smartphone time, was associated with greater anxiety, at both baseline and one month later. Affective and affiliative responses were less positive for online social interactions compared to in-person interactions. Anxiety, but not depression, was associated with feeling less happy, but not less included, after social interactions. Affective and affiliative responses to in-person, but not online, social interactions were negatively associated with depression across the 1-month study period. Finally, social sensitivity moderated the relationship between affective and affiliative responses to social media interactions and depression at baseline. Overall effect sizes were small.

CONCLUSIONS: These findings emphasize the need to investigate individual factors influencing for whom online social interaction is harmful or beneficial. To do so, this study provides a novel, ecologically valid tool for understanding young people’s momentary responses to online and offline social interactions, as well as initial evidence for stronger associations between in-person than online social interaction responses and mental health for older adolescents and emerging adults. It also introduces evidence of social sensitivity as a potential, developmentally relevant vulnerability to the effects of online social interaction. Further research is needed in younger adolescent populations over longer timeframes.

PMID:42574044 | DOI:10.2196/94753

J Mater Chem B. 2026 Aug 10. doi: 10.1039/d6tb00880a. Online ahead of print.

ABSTRACT

Early diagnosis of Alzheimer’s disease (AD) remains a major clinical challenge, particularly during the mild cognitive impairment (MCI) stage, where subtle cognitive changes overlap with normal ageing and reliable diagnostic indicators are limited. Plasma phosphorylated tau at threonine-181 (pTau-181) has emerged as a disease-specific biomarker associated with tau pathology and early neurodegenerative progression, with increasing evidence supporting its relevance for identifying individuals at risk of AD during the prodromal phase. However, accurate quantification of plasma pTau-181 is hindered by its extremely low concentration and the complex biochemical environment of blood. In this study, a label-free electrochemical impedance biosensor was developed for sensitive detection of plasma pTau-181 using a self-assembled two-dimensional MXene-gold nanorod (MXene-GNR) hybrid interface. Modification of a glassy carbon electrode with the MXene-GNR nanocomposite enhanced interfacial charge-transfer behaviour and increased the electroactive surface area by approximately 37%, enabling improved anti-pTau-181 immobilization and signal transduction. The biosensor exhibited a concentration-dependent impedance response toward pTau-181 over a wide dynamic range and achieved an ultrasensitive limit of detection of 12.561 fg mL^-1 in 10% plasma spiked samples while maintaining high analytical selectivity. Clinical plasma analysis demonstrated statistically significant differentiation of AD and MCI groups from healthy controls (p < 0.001), supporting the relevance of plasma pTau-181 measurement for assessing disease-associated cognitive impairment. These findings demonstrate the potential of the MXene-GNR electrochemical platform as a minimally invasive approach for plasma biomarker evaluation toward early AD diagnosis and monitoring.

PMID:42574041 | DOI:10.1039/d6tb00880a

Health Technol Assess. 2026 Aug;30(61):1-154. doi: 10.3310/GJAC1008.

ABSTRACT

BACKGROUND: Our aim was to develop and evaluate the electronic frailty index+, a prognostic tool, including four integrated prognostic-decision models, to stratify older people into subgroups for targeting key interventions.

METHODS: Prognostic model development, internal validation and external validation using large data sets and longitudinal cohort study data, with decision curve and health economic analysis.

POPULATION: Patients aged 65+ years.

KEY OUTCOMES: The 12-month outcomes for prognostic models: new home care package care home admission emergency department attendance/hospitalisation with fall/fracture all-cause mortality.

STATISTICAL METHODS: We developed and internally validated models for our key outcomes in one large data set. We used internal-external cross-validation for the home care model and full external validation for the remaining three models in a second large data set. We used CARE75+ to investigate additional predictive value of clinical measures practical for primary care.

DECISION CURVE ANALYSIS: We translated the prognostic models into a framework to support clinical decision-making.

HEALTH ECONOMIC EVALUATION: We integrated the falls prediction models with effect size estimates from network meta-analysis to examine potential cost savings.

RESULTS: We used data from 660,417 patients in SAIL, 88,947 in Connected Bradford and 252 CARE75+ participants. Model performance was promising in internal-external cross-validation, with average calibration slope 1.00 (95% confidence interval 0.99 to 1.01), average calibration-in-the-large -0.01 (95% confidence interval -0.02 to 0.01), average observed/expected ratio 0.99 (95% confidence interval 0.98 to 1.01) and average C-statistic 0.81 (95% confidence interval 0.81 to 0.81).

EMERGENCY DEPARTMENT ATTENDANCE/HOSPITALISATION WITH FALL/FRACTURE: Model performance was promising on internal and external validation, although with some evidence for overprediction of falls risk, with calibration slope 1.25 (95% confidence interval 1.24 to 1.27), calibration-in-the-large -0.931 (95% confidence interval -0.938 to -0.920), observed/expected ratio 0.43 (95% confidence interval 0.42 to 0.44), C-statistic 0.83 (0.82 to 0.83).

CARE HOME ADMISSION: Model performance was promising on internal validation, but it showed some miscalibration on external validation, with calibration slope 0.75 (95% CI 0.74 to 0.76), calibration-in-the-large -1.60 (-1.62 to -1.58) and observed/expected ratio 0.25 (95% CI 0.24 to 0.25), C-statistic of 0.86 (95% CI 0.86 to 0.86).

ALL-CAUSE MORTALITY: The model showed excellent performance across the full range of predicted risks on external validation, with average calibration slope 1.00 (0.98 to 1.01), average calibration-in-the-large -0.23 (-0.27 to -0.19), average observed/expected ratio 0.77 (0.75 to 0.79) and average C-statistic 0.83 (0.82 to 0.83).

ECONOMIC MODELLING: Modelling indicated that provision of multifactorial assessment and treatment for people with an annual falls risk of ≥ 40% has the largest cost reduction per targeted person (£1025).

DISCUSSION: All four prediction models have promising predictive performance, although some had evidence of overprediction of risk (miscalibration). Decision curve analysis indicates potential clinical utility, and economic modelling provides novel information for policy-makers and commissioners.

FUTURE WORK: Future research should include model impact studies to evaluate use of the models in routine care.

LIMITATIONS: We were unable to complete external validation of the home care prediction model.

STUDY REGISTRATION: This study is registered as ClinicalTrials.gov ID NCT04113174.

FUNDING: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 127905) and is published in full in Health Technology Assessment; Vol. 30, No. 61. See the NIHR Funding and Awards website for further award information.

PMID:42574037 | DOI:10.3310/GJAC1008