PLOS digital health最新文献

{"title":"Tailoring in eHealth lifestyle interventions targeting people with cardiometabolic conditions and lower socioeconomic position: A scoping review.","authors":"Sandra Van Mellaert, Martha S Kreuzberg, Iris Ten Klooster, Bert-Jan F van Beijnum, Jan N van Rijn, Monique Tabak","doi":"10.1371/journal.pdig.0001713","DOIUrl":"10.1371/journal.pdig.0001713","url":null,"abstract":"<p><p>eHealth interventions can support healthy lifestyle change for preventing and managing cardiometabolic conditions. While most eHealth interventions are designed for the general population, these conditions are more prevalent in people from lower socioeconomic backgrounds. Tailoring interventions to the needs and characteristics of this population can increase adherence and engagement, thereby enhancing the overall intervention effectiveness. However, evidence on how to tailor eHealth interventions to users from low socioeconomic backgrounds remains limited. Therefore, this scoping review examines the tailoring approaches implemented within eHealth lifestyle interventions targeting people with cardiometabolic conditions and low socioeconomic position (SEP). We focus on what is being tailored, the tailoring variables, the algorithms used for tailoring, and how the tailoring approaches are evaluated. Using keywords related to low SEP, cardiometabolic conditions, eHealth interventions, lifestyle, and tailoring, we searched electronic databases including Scopus, Web of Science, PubMed, and PsycINFO. We identified 43 eligible articles, with 27 unique eHealth lifestyle interventions targeting primarily diet and exercise. The literature shows a variety of tailoring approaches, albeit with a trend towards tailoring to socioeconomic factors at the design stage. Most interventions (n = 26/27, 96%) used rule-based algorithms for tailoring, primarily through functions such as feedback selection (n = 17/27, 63%) or variable substitution (n = 16/27, 59%). Although evaluation of tailoring was missing from most studies (n = 15/27, 55%), the importance of sociocultural relevance, appropriate language, and health literacy-sensitive design was highlighted. These findings suggest that while tailoring is present in many interventions, the current approaches remain limited in the facilitating technology and dynamic adaptations to SEP-specific needs. Thus, future research should investigate the application of more advanced, but reproducible tailoring algorithms and rigorously evaluate the impact of different tailoring methods on intervention effectiveness. To synthesize our findings, we assembled a framework that encapsulates the key concepts from our review, in combination with envisioned future work.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 9","pages":"e0001713"},"PeriodicalIF":7.7,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13568521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148946265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GPT-4 improves sex-specificity in cardiovascular patient education but may perpetuate gender biases: A mixed-methods audit.","authors":"Samah Khan, Georgeta Vaidean","doi":"10.1371/journal.pdig.0001122","DOIUrl":"10.1371/journal.pdig.0001122","url":null,"abstract":"<p><p>Patient education materials for cardiovascular disease (CVD) prevention frequently omit clinically important differences in disease manifestation, risk, and prevention between sexes. Socially constructed gender norms further shape how health information is communicated and received. Large Language Models (LLMs) like GPT-4 offer a potential tool for personalizing health communication, but their capacity to incorporate evidence-based sex differences without introducing or perpetuating gender biases is unknown. We identified seven publicly available English-language CVD prevention handouts from major health organizations. Using the GPT-4 API in August 2025, we generated sex-specific revisions for a 55-year-old male and female audience via standardized prompts. We provided structured prompts instructing the model to include evidence-based, sex-specific risk factors and symptoms. Original and revised materials were evaluated using Flesch-Kincaid Reading Ease, a novel 10-point sex-inclusivity checklist, and qualitative thematic analysis using a reflexive approach informed by thematic analysis principles. GPT-4 revisions substantially improved sex-inclusivity scores (Original median: 3.0/10, IQR = 0.0-3.0; Male-tailored median: 7.0, IQR = 7.0-8.5; Female-tailored median: 10.0, IQR = 10.0-10.0). Readability was maintained. However, qualitative analysis identified instances where outputs introduced content inconsistent with an equity-oriented framework: female-tailored content occasionally framed female symptoms as deviations from a male norm, male-tailored content frequently omitted erectile dysfunction as a CVD risk marker despite explicit prompting, and both versions at times reflected gendered social stereotypes (e.g., \"bottling up emotions\") rather than clinical evidence. LLMs can rapidly improve sex-specificity in patient education materials but may also reproduce gender stereotypes and linguistically biased framings present in their training data. Their integration into clinical content development requires critical human oversight to support, rather than undermine, health equity goals.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 9","pages":"e0001122"},"PeriodicalIF":7.7,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13561376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148931303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atrial fibrillation and retinal imaging-based oculomics: A cross-sectional and longitudinal analysis of two large cohorts.","authors":"Josef Huemer, Kelsey V Stuart, Yukun Zhou, Dominic J Williamson, Peter Woodward-Court, Alastair R Mobley, Arvind Chandratheva, Florian Berger, Nay Aung, Steffen E Petersen, Anthony P Khawaja, Axel Petzold, Jugnoo Rahi, Alastair K Denniston, Dipak Kotecha, Pearse A Keane, Praveen J Patel, Siegfried K Wagner","doi":"10.1371/journal.pdig.0001661","DOIUrl":"10.1371/journal.pdig.0001661","url":null,"abstract":"<p><p>We investigate the association between multimodal retinal imaging parameters with prevalent and incident atrial fibrillation (AF). For this cross-sectional and longitudinal analysis we utilized data from two independent cohorts: AlzEye (retrospective, age ≥ 40 years, 2008-2018) and UK Biobank (UKB; prospective, age 40-69 years, recruited 2006-2010). Retinal sublayer thicknesses and retinovascular indices were extracted from macular optical coherence tomography and color retinal photography, respectively. AF was ascertained via self-report and hospital admissions, association between retinal metrics with prevalent and incident AF were estimated using mixed-effects linear and frailty models. In AlzEye (n = 50,651), prevalent AF (n = 5,375; 11%) was associated with significantly smaller retinal venular calibre (-0.05 µm, 95% CI: -0.08, -0.03) and thinner macular retinal nerve fibre layer (-0.69 µm, 95% CI: -0.91, -0.46), ganglion cell-inner plexiform layer (mGCIPL; -1.96 µm, 95% CI: -2.34, -1.58), inner nuclear layer (-0.57 µm, 95% CI: -0.75, -0.38), and photoreceptor layer (-0.93 µm, 95% CI: -1.38, -0.48). Venular fractal dimension was lower (-0.08, 95% CI: -0.11, -0.06), while arteriolar fractal dimension was higher (0.05, 95% CI: 0.02, 0.08) in individuals with AF. Evidence of thinner mGCIPL was replicated in UKB (-1.19 µm, 95% CI: -1.73, -0.65). In UKB (n = 39,013 baseline without AF), 838 developed AF over a mean of 4.0 years. Adjusting for sociodemographic, clinical and lifestyle factors, each SD decrease in mGCIPL thickness was associated with a significant 27% increased hazard of AF (95% CI: 5%, 54%). Differences in the inner retinal sublayers, corresponding to retinal ganglion cells, are associated with prevalent and incident AF across two large independent cohorts, supporting the potential role of widely-accessible retinal imaging for early detection and better management of AF.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 9","pages":"e0001661"},"PeriodicalIF":7.7,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13561364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148931372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging prompt-driven generative AI for systematic reviews in digital psychiatry: A stage-matched comparative proof-of-concept for healthcare researchers and clinicians.","authors":"Karisa Parkington, Jithin T Joseph, Katie Musleh, Marianne Rouleau-Tang, Annabelle Persaud, Alice Rueda, Bazen Gashaw Teferra, Huda F Al-Shamali, Wanying Mao, Fatemeh Gholamali Nezhad, Lisa Burback, Yanbo Zhang, Rakesh Jetly, Eric Vermetten, Candice Monson, Sri Krishnan, Richard J Zeifman, Andrew Greenshaw, Wendy Lou, Venkat Bhat","doi":"10.1371/journal.pdig.0001666","DOIUrl":"10.1371/journal.pdig.0001666","url":null,"abstract":"<p><p>The scale and pace of evidence generation in digital psychiatry increasingly exceed the capacity of traditional systematic literature review (SLR) methods. Large language models (LLMs) are gaining traction in evidence synthesis, yet limited guidance exists for integrating generative AI into transparent, reproducible SLR workflows. To develop and evaluate a prompt-driven, modular decision-making framework for adaptable SLR workflows in digital psychiatry, and to compare stage-matched performance relative to a consensus-based human reference process. We conducted a mixed-methods, stage-matched comparative evaluation of three GPT-5 mode variants (Auto, Agent, and Deep Research) against a consensus-based human-led SLR workflow using a registered digital psychiatry review (PROSPERO CRD42025648122) as a case example. Nine SLR tasks were implemented within the ChatGPT interface using structured RISEN prompts: preliminary searches; research question, eligibility criteria, and search strategy development; article screening; data extraction; article summarization; critical appraisals; and descriptive results synthesis. Performance was evaluated using task-specific assessments of accuracy, sensitivity, specificity, inter-rater agreement, reporting compliance, hallucination monitoring, and workflow feasibility. All GPT-5 mode variants were feasible across SLR stages, although performance varied by task and mode. No fabricated study-level content was identified during structured hallucination auditing. Auto mode performed best for structured rule-based tasks requiring efficiency and implementation-ready outputs (e.g., eligibility criteria). Agent mode excelled in conceptual integration and interpretive tasks (e.g., research question generation, critical appraisals, descriptive synthesis). Deep Research mode most closely approximated human reasoning for higher-order synthesis tasks, performing best in full-text article screening, article summarization, and narrative synthesis. Prompt-driven LLM workflows are feasible and semi-efficacious for selected SLR tasks in digital psychiatry when deployed within a modular, human-in-the-loop decision-support framework. Findings support task-specific deployment of an adaptable modular framework, enabling healthcare researchers and clinicians to modify LLM workflows according to methodological appropriateness, confidence in performance, and institutional review practices.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 9","pages":"e0001666"},"PeriodicalIF":7.7,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13561331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148931509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A feasibility study evaluating seismocardiography for the detection of heart failure.","authors":"Ahmad Agam, Emil Korsgaard, Troels Yding Haugstrup, Kasper Janus Grønn Emerek, Maria Weinkouff Pedersen, Massar Omar, Jacob Eifer Møller, Kristian Kragholm, Samuel Emil Schmidt, Peter Søgaard","doi":"10.1371/journal.pdig.0001685","DOIUrl":"10.1371/journal.pdig.0001685","url":null,"abstract":"<p><p>The study aimed to develop a seismocardiograph (SCG)- based algorithm and assess its diagnostic performance in the detection of heart failure (HF). A total of 218 subjects were included: 198 with suspected HF and 20 with known HF with reduced ejection fraction (HFrEF) were included for testing only. Assessments were conducted using SCG, N-terminal pro b-type natriuretic peptide (NT-proBNP), electrocardiogram, NYHA classification and echocardiography. SCG-based algorithms, \"AnyHF score\" were developed to identify all subtypes of HF and \"HFrEF-score\" to identify HFrEF. Diagnostic accuracy was assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver-operating characteristic curve (AUC-ROC). The AnyHF score demonstrated an AUC of 82%, sensitivity of 90.9%, specificity of 43.8%, NPV of 87.5% and PPV of 52.6% in detecting HF versus no HF. A balanced comparative analysis was performed between NT-proBNP and the HFrEF-score for detecting HFrEF versus no HF. NT-proBNP demonstrated an AUC of 94.7%, sensitivity 94.1%, specificity 68.8%, NPV 99%, and PPV 27.1%. The HFrEF-score showed an AUC of 92.9%, sensitivity 88.2%, specificity 92% (p < 0.001), NPV 98.4%, and PPV 57.7% (p = 0.007). The SCG-scores also categorized 71 patients (51%) from the no HF group, referred on suspicion of HF, as minimal risk of HF. This study suggests that SCG has potential to aid in the diagnostic process of HF. The SCG-based algorithms \"AnyHF-score\" and \"HFrEF-score\" demonstrated high diagnostic performance in identifying HF.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 9","pages":"e0001685"},"PeriodicalIF":7.7,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13561325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148932043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topologically distinct intratumoral heterogeneity scores for predicting high-risk pathological grades in invasive lung adenocarcinoma: A multicenter study across four institutions.","authors":"Shanyue Lin, Yudian Mao, Qian He, Wanyin Qi, Sanhong Zhang, Wei Li, Zhichao Zuo","doi":"10.1371/journal.pdig.0001646","DOIUrl":"10.1371/journal.pdig.0001646","url":null,"abstract":"<p><p>High-risk subtypes of invasive lung adenocarcinoma (IAC), particularly micropapillary- or solid-predominant patterns, are closely associated with poor prognosis. This multicenter retrospective study developed and validated a predictive model for the preoperative identification of these high-risk subtypes using topologically distinct intratumoral heterogeneity (ITH) scores derived from CT images. The study included 1,051 patients with IAC. Two complementary ITH scores were developed: a two-dimensional ITH score, which integrated local radiomics features with global pixel distribution patterns on the largest cross-sectional CT slice, and a three-dimensional ITH score, which extended this quantification across the entire tumor volume. Clinicoradiological features and ITH scores were incorporated as model inputs to construct six base machine learning classifiers and a final stacking ensemble classifier. Model interpretability and robustness were evaluated using SHapley Additive exPlanations (SHAP)-based ablation analyses. An independent dataset from The Cancer Imaging Archive (TCIA) was used for external validation to investigate associations between ITH scores and pathological characteristics, genomic features, recurrence-free survival, and overall survival. The stacking ensemble classifier achieved the best predictive performance, with an area under the receiver operating characteristic curve of 0.875, outperforming models based solely on radiomics features (0.834) or clinicoradiological features (0.792). SHAP analysis identified the 3D ITH score as the most influential contributor to model output, and TCIA validation showed that higher 3D ITH scores were associated with more aggressive tumor biology and poorer survival outcomes. The topologically distinct 3D ITH score may provide a clinically meaningful imaging biomarker for preoperative risk stratification in IAC.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 9","pages":"e0001646"},"PeriodicalIF":7.7,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13552742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148914132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for blood glucose prediction: Reproducibility challenges and factors affecting differential performance.","authors":"Baiying Lu, Biratal Wagle, Zhaohui Liang, Yanjun Cui, Temiloluwa Prioleau","doi":"10.1371/journal.pdig.0001633","DOIUrl":"10.1371/journal.pdig.0001633","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 9","pages":"e0001633"},"PeriodicalIF":7.7,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13541153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Socioeconomic determinants of malaria in Ugandan children: An interpretable machine learning approach for public health policy.","authors":"Cleiane Gonçalves Oliveira, Marcos Flávio S V D'Angelo, Matheus P Libório, Marcelo Perim Baldo, Hasheem Mannan","doi":"10.1371/journal.pdig.0001152","DOIUrl":"10.1371/journal.pdig.0001152","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 9","pages":"e0001152"},"PeriodicalIF":7.7,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13541157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deceptive bias measurement in deep learning: Assessing shortcut reliance in TCGA cancer models.","authors":"Farnaz Kheiri, Shahryar Rahnamayan, Masoud Makrehchi","doi":"10.1371/journal.pdig.0001165","DOIUrl":"10.1371/journal.pdig.0001165","url":null,"abstract":"<p><p>Machine learning bias is a persistent challenge because it can create unfair outcomes, limit generalization, and reduce trust in real-world applications. A key source of this problem is shortcut learning, where models exploit signals linked to sensitive attributes, such as the data source or collection site, instead of relying on task-relevant features. To address this, we propose the Deceptive Signal metric, a novel quantitative measure designed to assess the extent of a model's reliance on hidden shortcuts during the learning process. This metric is derived via the Deceptive Bias Detection pipeline, which isolates shortcut dependence by contrasting the model's behavior under two controlled conditions: (1) Full Exclusion, where a sensitive subgroup is completely removed from training; and (2) Partial Exclusion, where the model has limited access to specific classes within that subgroup. By calculating the behavioral shift between these settings, the Deceptive Signal metric provides a quantitative estimate of the model's susceptibility to learning task-irrelevant patterns. In experiments with the TCGA histopathology dataset, our metric successfully quantified substantial dependencies on center-specific artifacts in models trained for cancer classification.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 9","pages":"e0001165"},"PeriodicalIF":7.7,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13541255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Community-based malaria surveillance in Cambodia using a mobile reporting app for village malaria workers: An implementation case study.","authors":"Pengby Ngor, Huy Rekol, Siv Sovannaroth, Po Ly, Eng Thyda, Sok Kimleng, Yoem Rattana, Ou Vunsokserey, Hem Vanna, Ran Sany, Chang Pumeoung, Steven Mellor, Rady Try, Pascal Ringwald, Richard J Maude, Lisa J White","doi":"10.1371/journal.pdig.0001626","DOIUrl":"10.1371/journal.pdig.0001626","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 9","pages":"e0001626"},"PeriodicalIF":7.7,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13541183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}