Blood glucose prediction is a critical component of next-generation diabetes technologies, such as artificial pancreas systems, where reliable performance is essential for safety and effectiveness. Although deep learning methods have achieved promising advances in this area, a critical gap remains in understanding the reproducibility and generalizability of these methods. To contextualize the gap, this study reviewed 67 recent papers that proposed a deep learning method for glucose prediction to identify key reproducibility challenges. Next, we adopted a standardized framework, encompassing technical, statistical, and conceptual reproducibility evaluations, to experimentally assess the reproducibility of eight representative deep learning methods. To achieve this, we reimplemented and evaluated these eight deep learning methods using over 1.36 million continuous glucose monitoring samples (5,061 days) from 128 individuals with type 1 diabetes across three public datasets: OhioT1DM, DiaTrend, and T1DEXI. We found that even though these models demonstrated good technical and statistical reproducibility, their conceptual reproducibility-the ability to generalize to datasets with different diabetes management patterns-was limited. Further analyses revealed that each model's overall prediction performance was strongly influenced by individual glycemic control, with higher prediction errors observed among participants with lower time with blood glucose in the target range (70-180 mg/dL). This study identified key reproducibility challenges associated with current blood glucose prediction methods within type 1 diabetes populations, highlighting the need for increased transparency, dataset diversity, standardized evaluation practices, and code accessibility to ensure reproducible and reliable models for blood glucose prediction.
Malaria remains a critical global health crisis, placing a disproportionate burden on children under five in Uganda. To transition from broad surveillance to targeted intervention, this study applies interpretable machine learning to identify key socioeconomic predictors of malaria using the 2018-2019 Uganda Malaria Indicator Survey. By employing Random Forests for feature selection and Decision Trees for classification, we addressed the inherent class imbalance using robust metrics such as the F2-score, Matthews Correlation Coefficient, and Precision-Recall Curve. Specifically, the Random Under-Sampling technique enabled the model to achieve a Recall of 77%, prioritizing the reliable detection of true positives over simple accuracy. The analysis highlights the hierarchical importance of determinants such as household size, mosquito net ownership, and maternal education. The study's defining contribution is the extraction of explicit "if-then" rules that visualize how these factors combine to create risk profiles, particularly revealing distinct disparities across regions such as Busoga and West Nile. These interpretable findings empower policymakers with actionable, evidence-based insights, moving beyond simple prediction to facilitate the design of structural and region-specific public health strategies.
To support malaria elimination, Cambodia implemented a locally developed, nationally integrated, community-based mobile digital surveillance system enabling real-time, case-based detection, reporting and response. This implementation case study describes the development, national deployment and operational performance of an Android-based mobile app used by Village Malaria Workers (VMWs) and Mobile Malaria Workers (MMWs) integrated within the national Malaria Information System (MIS). Developed collaboratively with VMWs, the system enables real-time, geolocated case reporting with offline functionality, built-in validation checks, centralized device management and automated analytics. Evaluation employed three data sources: routine surveillance data (2018-2024), a contextual survey of VMW/MMWs in three high-risk provinces (2024), and a national technical usability survey (2025). Outcomes included reporting timeliness, completeness, surveillance performance indicators aligned with the national malaria elimination framework and user experience. Following national implementation in 2017- 2018, data completeness and timeliness improved. Between 2018 and 2024 reporting coverage increased from 19% to 99% and data completeness has remained ≥99% since 2019. Case notification within 24 hours increased from 2% to 99% and foci investigation within 7 days from 0% to 98%. Malaria testing rates expanded from 135,664 to 715,277 per year. Intensified surveillance was concurrent with a decline in active malaria foci from 129 in 2020 to 0 in 2024. A contextual evaluation including 88 VMW/MMWs showed strong support for surveillance activities, including reporting and data analysis at the community level. The technical evaluation survey found that 94% (904/966) of VMWs were very satisfied/satisfied with the app, with technical problems experienced never/rarely by 82% (795/966). Key operational challenges included intermittent internet connectivity, limited electricity supply, and transport constraints, although offline functionality and data synchronization prevented data loss. Cambodia's experience demonstrates the feasibility and sustainability of large-scale digital surveillance in resource-constrained settings and highlights the importance of integration, training and sustainability through local ownership.
Accurate assessment of body composition is essential for monitoring health status, fitness progress, and disease risk. Traditional methods such as air displacement plethysmography (BODPOD) and bioelectrical impedance analysis (BIA) are widely used to estimate body fat percentage (BF%), fat mass (FM), and fat-free mass (FFM), but can be costly or inaccessible. Smartphone applications utilizing computer vision (CV) offer a promising alternative. This study evaluated the relative agreement and same session repeatability under standardized conditions of a smartphone-based CV application (CVapp) compared to BODPOD and the InBody BIA device. Forty-nine adults (ages 18-70; 29 females, 27 racial and ethnic minority participants) completed two consecutive measurements using BODPOD, InBody, and CVapp in a single session. Differences in BF%, FM, and FFM estimates were analyzed using repeated-measures ANOVA. Agreement metrics included mean absolute error (MAE), root mean square error (RMSE), concordance correlation coefficients (CCC), and Bland-Altman analysis. Subgroup analyses examined differences by sex and minority status. The CVapp yielded higher BF% (mean=+2.2%, P = 0.004) and FM (+1.5 kg, p = 0.015) compared to BODPOD, and lower FFM than InBody (mean=-1.84 kg, p = 0.043). The CVapp agreement with BODPOD (MAE = 4.0, RMSE = 5.0, CCC = 0.86) was weaker than with the InBody (MAE = 3.3, RMSE = 4.3, CCC = 0.89), with wider limits of agreement. All methods showed excellent within session repeatability under standardized conditions (ICC > 0.99). A significant Sex×Method interaction was observed for BF% (p < 0.001), FM (p < 0.001), and FFM (p = 0.013), with females showing greater overestimation of BF% (mean=+3.9%) and FM (mean=+2.7 kg, p < 0.001), and underestimation of FFM (mean=-2.5 kg, p < 0.001) by the CVapp. Among Minority participants, BF% and FM estimates from the CVapp were significantly higher than BODPOD (mean=+2.7%, p = 0.009; mean=+1.8 kg, p = 0.031), and FFM was lower (mean=-1.8 kg, p = 0.035), with significant Method×Minority Group interactions for FM and FFM (p < 0.03). These findings support cautious use of CVapp-derived values for within-person monitoring under similar conditions but suggest they should not be treated as interchangeable with established comparator methods or used as standalone diagnostic estimates of body composition. Future efforts should address the need for more accurate, transparent, and equitable algorithms that are validated across diverse populations and testing contexts.
Digital health technologies (DHTs) such as patient portals, mobile applications, and electronic health records can improve access to healthcare, self-management and care coordination. However, their adoption remains inconsistent. This study systematically reviews the technological, psychological, social, cultural, health-related and environmental factors influencing DHT adoption. This protocol-registered review (PROSPERO: CRD420251056883) was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines. A search of multidisciplinary databases was conducted through the EBSCO Discovery Service (EDS) to identify peer-reviewed English primary studies published between 2015 and 18 June 2025. Although studies published from 2015 onward were searched, only studies published from 2020 onward were retained for synthesis. Two researchers independently applied the Sample, Phenomenon of Interest, Design, Evaluation, Research type (SPIDER) framework to screen articles and extract data. As Covidence operationalises screening using the Population, Intervention, Comparison, Outcome, Study type (PICOS) framework, SPIDER elements were mapped to PICOS to ensure consistency across screening and extraction. Methodological quality was appraised using the Mixed Methods Appraisal Tool (MMAT) to inform interpretation. Data were synthesised using a structured thematic analysis workflow informed by the Thematic Analysis Matrix proposed by Zairul, which operationalises the thematic analysis principles described by Braun and Clarke. Coding, category development and theme generation were managed using ATLAS.ti (Version 24). Eighty-two studies published between 2020 and 2025 met the inclusion criteria. Five themes were identified: (1) access, equity and affordability; (2) usability, engagement and user empowerment; (3) trust, privacy and governance; (4) integration, workforce and sustainability; and (5) clinical effectiveness and quality of care. The findings showed that adoption of DHTs is a multi-layered process shaped by multidisciplinary factors. This review provides researchers, policymakers and healthcare providers with theoretical and practical insights to support sustainable, effective and equitable DHT adoption and to guide the development of future strategies.
We aimed to systematically analyze the historical evolution of artificial intelligence (AI) in end-stage renal disease (ESRD) management and propose a developmental framework to map its progression from assistive tools to cognitive collaborators. A systematic review was conducted following PRISMA 2020 guidelines, identifying 100 eligible studies from PubMed, IEEE Xplore, and Web of Science. A three-stage analytical framework was applied to chart the technological evolution of AI in ESRD care: (1) rule-based assistive tools, (2) data-driven learning systems, and (3) emerging large language model- and agent-based cognitive systems. A total of 100 studies were included, all focusing on patients with end-stage renal disease managed through hemodialysis, peritoneal dialysis, or kidney transplantation. Three primary application domains were identified: risk prediction (49.0%), diagnostic support (25.0%), and monitoring and management (26.0%). The analytical framework revealed a developmental progression from interpretable rule-based systems (Stage 1) to high-performing data-driven models (Stage 2), which achieve clinically relevant metrics (e.g., area under the receiver operating characteristic curve [AUC] 0.80-0.90) but often lack external validation. Emerging large language model- and agent-based systems (Stage 3) demonstrate notable versatility but introduce new challenges related to reliability, factual accuracy, and safety alignment. The proposed three-stage framework clarifies AI's technological and functional evolution in ESRD care. This perspective highlights a critical need to bridge the gap between high-performance modeling and validated clinical utility. Future work should focus on robust external validation and the development of frameworks for the safe, reliable, and ethical deployment of next-generation cognitive AI agents.
Despite the promising potential of Artificial Intelligence (AI) models to enhance health equities for persons with disabilities, poorly designed applications risk exacerbating inequities. To address this, we conducted a narrative review using disability justice frameworks as an analytical lens to evaluate how AI applications operationalize disability and health equity across various domains. AI models demonstrate potential in early intervention, personalized care, equitable resource allocation, assistive technologies, and health surveillance. However, most AI models rely primarily on biomedical and functional data, often trained on biased datasets that neglect social and structural determinants of disability. As a result, these applications insufficiently capture broader dimensions of wellbeing, including capabilities, recognition, and structural justice, limiting their effectiveness in addressing health inequities. To advance health equity for persons with disabilities, AI applications must incorporate disability-inclusive datasets, participatory co-design with persons with disabilities, auditing for fairness, and real-world validation. Integrating clinical, social, and structural dimensions in a justice-oriented framework can help AI models move beyond narrow biomedical models toward more inclusive, context-sensitive, and equitable health systems.
Stress and diabetes distress impact glycemic control, self-care, and health outcomes in people living with diabetes, but current diabetes technologies lack easy-to-use, non-invasive methods to detect these states. The objective was to identify vocal features associated with stress and diabetes distress in people living with diabetes. Thus, we analyzed data from 679 adults with diabetes (381 women, 298 men), recruited via the global vocal biomarker screening platform Colive Voice. Participants recorded a standardized 30-second text. Vocal features were extracted using DisVoice across phonation, prosody, articulation, and phonological domains. Associations between vocal features and stress (self-reported, scale: 0-4) and diabetes distress (Problem Areas in Diabetes (PAID) questionnaire; categories: PAID<20, 20 ≤ PAID<40, 40 ≤ PAID<60, PAID≥60) were analyzed separately for men and women. Multivariate ordinal logistic regression models were adjusted for age, language, diabetes type, and HbA1c, with False Discovery Rate correction.In women, results suggest that stress was associated with 25 voice features across all domains, while distress was linked to one articulatory feature, suggesting broader effects of stress. In men, both stress (25 features) and distress (18 features) affected multiple domains, but with distinct acoustic patterns. No voice features were shared between stress and distress. These patterns were consistent across age, diabetes type, and language.To conclude, voice changes reflect stress and diabetes distress in people with diabetes, each characterized by distinct acoustic features and additional sex-specific signatures. These findings highlight the potential of vocal biomarkers to differentiate and monitor these emotional states, supporting more timely and personalized interventions to improve diabetes outcomes.
The automated discovery of structural patterns in macromolecular complexes remains a central challenge in cryo-electron tomography, particularly in highly heterogeneous datasets. Although fully unsupervised clustering methods have shown promise in grouping subtomograms by structural similarity, they often ignore a crucial source of information: the partial ground truth routinely available to structural biologists from prior studies or manual annotations. In this work, we propose a semi-supervised structural discovery framework that utilizes partial supervision to guide clustering without compromising the ability to uncover previously unknown structures. At the core of our method is a label-anchored probabilistic clustering mechanism that seeds the latent space using a small subset of labeled examples and refines it through a multi-resolution consensus strategy based on PCA-space voting. This is complemented by an entropy-based confidence scoring scheme that attenuates the influence of ambiguous samples, as well as a feature propagation procedure that extends structural labels to low-confidence regions using local similarity in feature space. Together, these components create a stable and adaptive pipeline capable of discovering both known and novel structures. Our approach is efficient, requires as little as 1% of labeled data per class, and consistently produces clearer, more interpretable feature embeddings compared to fully unsupervised methods, with well-separated clusters from the very first iterations. Extensive experiments on simulated and realistic tomographic datasets demonstrate that this semi-supervised strategy significantly improves clustering performance, robustness, and biological relevance in cryo-electron tomography analysis. These methods are integrated as extensions to the existing Deep Iterative Subtomogram Clustering Approach pipeline, enhancing its capability for guided structural discovery.
Digital twin technologies (DTTs) are increasingly applied in cardiovascular medicine to support personalized treatment. At the same time, growing evidence demonstrates sex differences in cardiovascular anatomy, physiology, disease presentation, and outcomes. Whether current cardiovascular DTTs adequately incorporate these sex-specific characteristics is unclear. This narrative review examines how sex bias and inclusivity are addressed within cardiovascular DTTs and identifies where sex-related bias may arise within different components of DTT. A six-dimensional digital twin framework is introduced to review DTTs. We focus on three cardiovascular domains: coronary artery disease, aortic valve stenosis, and atrial fibrillation. For each domain, we assessed sex representation in modeling assumptions, data interpretation, clinical outputs, and validation studies. Within three domains, physiological assumptions, boundary conditions, interpretation thresholds, and validation cohorts, models are frequently derived from sex-skewed populations. DTTs estimating noninvasive fractional flow reserve are predominantly validated in male-dominated cohorts, potentially resulting in lower precision in women. In contrast, validation studies for DTTs in TAVI planning show variable sex distributions, with some cohorts being female-skewed and others male-skewed, raising questions about their generalizability across sexes. In both model-based DTTs, sex bias may arise from generalized boundary conditions. Electro-anatomical mapping systems for atrial fibrillation are susceptible to sex bias within measurement methodology. Uniform clinical thresholding and outcome selection bias further add to sex-bias in DTTs. Current cardiovascular DTTs insufficiently account for sex-specific cardiovascular characteristics, risking underperformance in underrepresented populations. Incorporating sex-aware physiological parameters, sex-stratified validation, balanced datasets, and transparent reporting should be considered minimal standards for future clinical implementation.
Intratumor heterogeneity (ITH) is a critical factor influencing cancer progression, therapeutic response, and the development of drug resistance. Despite its importance, the lack of a definitive gold standard for ITH quantification has hindered consistent clinical application. To bridge this gap, we developed ITHindex, a web-based, research-enabling platform that integrates a pragmatic subset of 17 user-accessible ITH algorithms within the R Shiny framework. The tool supports diverse data modalities, including somatic mutation, copy number variation, transcriptomic, proteomic, and methylation profiles. By analyzing 11,242 samples across 32 cancer types from The Cancer Genome Atlas (TCGA) and 4,904 samples from cBioPortal, alongside 398 paired tissue and plasma samples, we validated the platform's utility in quantifying ITH and characterizing the relationships between diverse metrics. ITHindex streamlines the computational pipeline, providing researchers with a robust tool for systematic ITH investigation and data-driven biomarker discovery. The ITHindex server is freely accessible at https://shinyapps.brbiotech.com/app/ithindex.
Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are essential for the diagnosis, clinical decision-making, and prognosis of cardiovascular disease. However, accurate assessments of LVEF and GLS by echocardiography are hampered by inter-observer variability, time-consuming, and labor-intensive. This study aimed to develop an automated method to accurately and rapidly assess LVEF and GLS. Based on the datasets of 500 patients (1,500 videos) from the internal center and 363 patients (1,089 videos) from four external centers, we successfully developed a dual-flow convolutional neural network called Echo-DFCNN, which allowed for synchronous acquisition of LVEF and GLS. We evaluated the performance of the Echo-DFCNN in a cardiac magnetic resonance (CMR) validation dataset composed of 67 patients. On the internal test dataset, the AI and manual measurements of LVEF demonstrated a median absolute error of 3.02% and a mean absolute error of 3.94%. AI-predicted LVEF showed good agreement with manually measured LVEF, with an ICC of 0.927, a bias of 0.89%, and a LOA of -10.91 to 12.69. For GLS, the median absolute error and mean absolute error between AI and manual measurements were 1.43% and 1.83%. AI-predicted GLS exhibited high agreement with manually measured GLS (ICC = 0.913; bias = -1.22%, LOA = -5.12 to 2.68). In addition, Echo-DFCNN maintained good performance when applied to external validation datasets. In the CMR validation dataset, the AI model showed good agreement with CMR measurements for both LVEF and GLS. Echo-DFCNN achieves simultaneous and precise assessment of LVEF and GLS in the study cohorts, demonstrating its potential for robust performance across a wide range of cardiac functions, different image qualities, and machine types.
Systematic reviews (SRs) are key to evidence-based medicine but are often labor-intensive, especially in the study selection step. This study assessed the use of large language models (LLMs) to automate SR study screening and selection. Five SR projects were included: two published therapeutic SRs (SR1-2), two ongoing emulated-trial SRs (SR3-4), and one economic evaluation SR (SR5). The total number of studies screened for each SR was 3,966, 3,147, 695, 3,096 and 485, respectively, with 20, 24, 46, 32 and 70 eligible studies. Three LLMs-Gemini 2.0 Flash, Llama 3.1, and Qwen 2.5-were evaluated using training sets (five studies), title/abstract datasets, and full-text datasets predicted as relevant. Prompts based on the PICOS framework were iteratively refined using a recall-first strategy. Outputs were compared with human reviewer classifications using recall, number needed to screen (NNS), and percentage reduced workload with 95% confidence intervals. In the title/abstract screening phase, Llama 3.1 and Gemini 2.0 Flash achieved consistently high recall (90.00%-100.00% and 90.48%-100.00%), with workload reduction of 61.92%-97.10% and 65.21%-97.03%, respectively. Qwen 2.5 achieved the highest workload reduction (76.16%-99.19%) but showed the lowest recall (76.67%-88.89%). In the full-text selection phase, Llama 3.1 achieved the highest recall (93.33%-100.00%) with workload reductions of 73.15%-97.41%, but slower processing time (approximately 2.3-3.6 minutes per document). Qwen 2.5 yielded lower recall (66.67%-89.71%), despite the highest workload reduction (80.82%-99.44%) and similarly slow inference times (approximately 3.0-4.2 minutes per document). Gemini 2.0 Flash balanced high recall (83.33%-100.00%) with substantial workload reduction (76.71%-98.91%) and markedly faster inference (approximately 4-8 seconds per document). LLMs-particularly Llama 3.1 and Gemini 2.0 Flash-can substantially reduce SR screening workload while maintaining high recall when guided by a recall-first prompting framework. Remaining challenges include reproducibility in closed-source models and generalizability across diverse SR topics.
Artificial intelligence (AI) tools are entering clinical practice at unprecedented speed. 1,357 AI/ML-enabled medical devices have received U.S. FDA clearance or approval, yet their impact on patient outcomes remains largely untested. We conducted a systematic analysis of all FDA-cleared AI/ML-enabled medical devices through December 5, 2025 using the FDA device database and the ACR Data Science Institute catalogue, with linked searches of ClinicalTrials.gov and PubMed to identify registered trials and publications. Of 1,357 cleared AI devices, only 34 (2.5%) were linked to registered prospective trials, 12 (0.9%) posted results, 12 (0.9%) had peer-reviewed publications, and only 3 (0.2%) evaluated patient-centered outcomes such as mortality, morbidity, or readmissions. Most studies (62%) employed observational designs with small, homogenous cohorts, limited subgroup analyses, and frequent exclusion of vulnerable populations. Structural barriers (including misaligned financial incentives, reliance on predicate-based regulatory pathways, and logistical challenges of multi-center trials) discourage rigorous evaluation. Internationally, FDA clearance often functions as a gateway for global deployment, raising ethical concerns when under-validated tools are introduced into low- and middle-income countries without contextual validation or safeguards. Regulatory approval has outpaced clinical validation, creating an ecosystem where innovation advances without accountability. The finding that only 0.2% of cleared devices have undergone evaluation for patient-centered outcomes reveals a profound validation gap and points to the need for evidence standards capable of keeping pace with the speed of regulatory clearance. Readiness should no longer be defined by FDA clearance alone, but by demonstrated, durable, and equitable benefit to patients.
In patients with congenital single ventricle physiology, the Bidirectional Glenn procedure is a critical intermediate step toward Fontan circulation. Despite technical advances, morbidity and mortality remain significant. While left pulmonary artery (LPA) stenosis is a recognized complication, the broader impact of 3D anatomical variations on outcomes remains poorly understood. We aimed to characterize morphological variability of the superior cavopulmonary connection (SCPC) following the Bidirectional Glenn and assess its relationship with clinical outcomes. Statistical Shape Modeling (SSM) was applied to cardiovascular magnetic resonance images from 29 single ventricle patients following Bidirectional Glenn, whose SCPC showed no clinically important SCPC or pulmonary arterial stenosis. A population-based 3D anatomical template was generated using the Deformetrica framework. Patient-specific shape deformations were quantified and correlated with clinical metrics-including SCPC pressure, oxygen saturation, intensive care unit (ICU) stay, and total hospital stay-via Partial Least Squares (PLS) regression. Associations were compared to correlations with conventional morphological parameters. SSM revealed 3D shape features significantly associated with SCPC pressure and ICU stay (p < 0.01). Lower SCPC pressure correlated with straighter, wider LPA geometry. In contrast, narrower-but without overt stenosis-LPA morphologies were associated with worse outcomes. Conventional geometric metrics did not show such correlations. No association was observed between SCPC shape and total hospital stay. This study provides a novel 3D shape-based characterization of SCPC anatomy in single ventricle patients post Bidirectional Glenn surgery and its link to outcomes. SSM-derived morphological biomarkers offer potential for improved risk stratification and surgical planning in congenital heart disease.
To evaluate the potential savings realized by replacing the 4-6 weeks consultation following cataract surgery with remote self-assessment. In addition, we evaluated the costs associated with adverse events (AEs) from a societal perspective and evaluated the current cost of AEs based on published literature and publicly available datasets. We determined the cost reduction when replacing the in-person 4-6-week postoperative consultation visit with remote self-assessment using our previous clinical trial, publicly available healthcare resource databases, and threshold analysis to determine the effect on cost if the incidence of AEs is affected by conducting an online self-assessment. We collected AE probabilities and costs of AEs based on published literature and databases. and performed a threshold analysis to demonstrate the financial impact if the incidence of AEs is altered versus the savings due to replacing the 4-6 weeks in-person consultation with remote self-assessment. Remote self-assessments resulted in an average savings between -€83 and -€92 per patient. Physical consultations were less costly only if >80% of remote self-assessments warranted an in-person follow-up. The mean cost of AEs per patient was estimated to be €16.35 and were attributed primarily to rare but severe complications, including endophthalmitis and retinal detachment. Having patients participate in a remote self-assessment following cataract surgery is expected to reduce costs under a wide range of assumptions. Due to the lack of published data regarding the utility loss due to AEs, quality of life was not included in our analysis; nevertheless, these cost savings should be balanced by the clinical impact of potentially missing relatively rare AEs. These findings contribute to the ongoing discussion regarding appropriate care following cataract surgery.
Africa faces a disproportionate burden of hypertension, with a prevalence of 27% compared to 18% in the Americas. Despite cost-effective lifestyle and medical interventions, the region suffers from poor detection, treatment, and control rates. Mobile health (mHealth) interventions show promise for hypertension management, however, implementation outcomes in African countries remain underexplored. To fill this gap, this systematic review assessed the acceptability, feasibility, and adoption rates of mHealth interventions among adults diagnosed with hypertension in Africa and identified various facilitators and barriers to its implementation. The review was conducted across nine databases, adhering to the World Bank's classification of low and middle-income countries (LMICs) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. The search was conducted in October 2023 and updated in January 2024 with no date restrictions. Studies evaluating mHealth interventions for hypertension management among African adults, using experimental, observational, or qualitative designs, and reporting Proctor's implementation outcomes were included. Studies involving individuals without diagnosed hypertension and pregnant women with preeclampsia, and African population living outside Africa were excluded. Risk of bias was evaluated using the ASSESS tool (A comprehenSive tool to Support rEporting and critical appraiSal of qualitative, quantitative, and mixed methods implementation reSearch Outcomes). Data was extracted independently in Covidence and conflicts were resolved by consensus. In the context of contemporary research practices, it is important to note that no large language models or other AI systems were utilized in the development of this paper. We found eighteen articles focusing on mHealth interventions for hypertension in Africa, exploring acceptability (n = 10), feasibility (n = 11), adoption (n = 4), and appropriateness (n = 2). Service outcomes including effectiveness (n = 3), efficacy (n = 1), patient-centeredness (n = 1), and scalability (n = 2) were also examined. Acceptability and effectiveness were the most frequently reported outcomes. However, feasibility challenges, mainly financial constraints, and cellular network issues, were prominent. Most studies were urban-centric, indicating a gap in understanding rural challenges. Risk of bias was low across all the included studies. Application-based mHealth interventions appears to have gained significant utilization in Africa, particularly in countries like Nigeria, Ghana, and South Africa, while SMS-based interventions show promise in reaching non-smartphone users. Despite successes, feasibility challenges persist, necessitating targeted interventions for financial constraints and network infrastructure. The limitations of this systematic review include exclusion of hypertension with comorbidities beyond diabetes and stroke. Moreover, most studies were confined to urban areas potentially limiting the scalability of these interventions in rural and non-urban regions in resource-constrained areas. Future research should aim to bridge the urban-rural gap and explore innovative solutions to enhance the feasibility, adoption, scalability, and sustainability of mHealth interventions for hypertension management across diverse African settings.
While effective against non-small cell lung cancer (NSCLC), PD-1 inhibitors can induce immune-related adverse events (irAEs), occurring in up to 15.2% of patients and potentially fatal. Currently, effective predictive biomarkers capable of simultaneously forecasting both irAEs and immune checkpoint inhibitor (ICI) responders remain elusive. This limitation hinders the safe clinical application of these agents. This study enrolled 333 advanced NSCLC patients treated with PD-1 inhibitor monotherapy or combination therapy. CT imaging features were extracted using radiomics and deep-learning approaches. Three unimodal and two multimodal models were constructed to predict irAEs (Grade ≥3) and ICI responders in parallel. The SHAP algorithm was used to identify clinical features contributing to the prediction of both irAEs and ICI responders. The CDML-DenseNet model, integrating clinical features with deep-learning-derived radiomics features (DenseNet), demonstrated superior performance in predicting irAEs (AUC = 0.85), outperforming single-modal radiomics models. For ICI responder prediction, the CDML-DenseNet model achieved an AUC of 0.866. The Prognostic Nutritional Index (PNI) was identified as a key feature in both irAEs and ICI responder prediction models. Patients who were non-responders to ICIs but experienced irAEs had significantly lower PNI (46.8 ± 8.779, P < 0.05) compared with ICI responders without irAEs. Our multimodal CDML-DenseNet model effectively predicts both irAEs and ICI responders in NSCLC patients receiving PD-1 inhibitors. This approach provides a novel framework for balancing immunotherapy efficacy and toxicity. Furthermore, the readily available and cost-effective PNI offers clinicians a practical tool to identify potential non-responders experiencing irAEs and to refine treatment decisions.
The increasing digitalization of healthcare systems presents both opportunities and challenges for patients. A key challenge lies in cultivating e-health literacy, defined as the capacity to locate, comprehend, appraise, and use digital health information and services. This study employs a mixed-methods approach to investigate social and structural factors perceived as shaping patients' capability to use digital health services. A mixed-method approach guided the design, data collection, data analysis, and synthesis. This included semi-structured interviews and an online survey to corroborate findings and enhance credibility. The objective of the study was to examine the perspectives and dispositions of members and representatives of patient organizations regarding digital health services identifying key factors associated with e-health literacy. Findings indicate that respondents view social factors as crucial. Motivation to engage with digital health services is seen as a pivotal factor to shape the development of e-health literacy. However, motivation is not solely an individual trait; it is shaped by social contexts and trust in digital systems. Ensuring the highest standards of data security and transparency is imperative for cultivating this trust. Furthermore, patients must feel a sense of autonomy regarding their personal health data to engage confidently with digital health services. Structured learning environments, offered by social actors such as governments, patient organizations, and health insurance providers, also play a crucial role. These insights are relevant for practitioners in healthcare and public administration. To develop e-health literacy, it is imperative to establish inclusive learning opportunities, ensure data protection, and cultivate patient empowerment. Government agencies, healthcare providers, and insurance companies should collaborate with relevant stakeholders to design and implement supportive measures that reflect patients' lived realities. This approach can help ensure that all individuals are equipped to participate meaningfully in a digitalized healthcare environment.
Social media use is a key risk factor for mental health symptomatology among emerging adults. Black emerging adults use social media frequently, where they are exposed to online racism, which may contribute to worse mental health. The goal of the current study was to examine the frequency of exposure to online racism as a mediator in the association between the frequency of social media use and depression and anxiety symptoms. Using a non-probability sample of 1005 monoracial Black emerging adults (Mage = 24.07, 50.6% women) from a larger investigation, participants completed an online survey, in which they reported their social media use, exposure to online racism, and mental health symptoms using established measures. Findings support mediation of associations for some social media platforms. More frequent Twitter (standardized indirect effect (SIE):.04, p = .027), Reddit (SIE:.10, p < .001), and TikTok (SIE:.05-.06, p = .005) use were associated with more frequent exposure to online racism, and more frequent exposure to online racism was associated with increased odds of depression and anxiety. Mediation was not supported for YouTube, Facebook, or Instagram. Clinicians should consider stress from online racism as a factor for clients with high use of certain social media platforms. Future research can explore whether platform features like content warnings, hate speech moderation, and bias response teams could also help reduce exposure.
