PLOS digital health最新文献

{"title":"Large language models are poor clinical administrators: An evaluation of structured queries in real-world electronic health records.","authors":"Eyal Klang, Vera Sorin, Panagiotis Korfiatis, Ashwin S Sawant, Robert Freeman, Alexander W Charney, Girish N Nadkarni, Benjamin S Glicksberg","doi":"10.1371/journal.pdig.0001326","DOIUrl":"10.1371/journal.pdig.0001326","url":null,"abstract":"<p><p>Large language models (LLMs) have shown promise in interpreting clinical free-text like provider notes. There is limited evidence on tabular electronic health record (EHR) tasks. Our objective was to evaluate the accuracy of LLMs on structured EHR administrative tasks using direct prompting, chain-of-thought (CoT) reasoning, and tool-enabled code generation. We evaluated nine LLMs randomly sampling from a real-world sampled dataset of 50,000 emergency department (ED) visits. Tasks were tested across 25 combinations of table sizes (5-25 rows and columns). Models were prompted directly or with CoT reasoning to return numerical answers. In the tool setting, models generated Python code, which was executed to retrieve answers. Accuracy was defined as the proportion of model outputs matching validated references. We also assessed JSON format compliance. Across 32,950 model queries, performance varied by model, task type, and prompting strategy. Direct prompting produced uniformly low accuracies. CoT prompting moderately improved performance, particularly for logical filtering, but results degraded significantly as table size increased. The tool-based strategy substantially improved accuracy. Smaller models and distilled reasoning variants had more frequent formatting and execution errors. In conclusion, for structured EHR tabular data extraction, direct and CoT prompting strategies resulted in limited accuracy and poor scalability, particularly as table size increased. Tool-based prompting, where models generated and executed Python code, achieved higher accuracy and valid output formatting. Structured data tasks in clinical workflows may require hybrid approaches that combine LLMs with code execution to ensure accuracy and consistency.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 5","pages":"e0001326"},"PeriodicalIF":7.7,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13152155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846949","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":"SleepDepNet: A multi-task transformer model for assessing sleep quality and depression risk from social media narratives.","authors":"Akshi Kumar, Saurabh Raj Sangwan, Aditi Sharma","doi":"10.1371/journal.pdig.0000859","DOIUrl":"10.1371/journal.pdig.0000859","url":null,"abstract":"<p><p>The relationship between sleep and mental health, particularly depression, represents a critical area of study with significant implications for individual well-being and public health. This work introduces SleepDepNet, a transformer-based multi-task learning framework designed to jointly model sleep quality and depressive sentiment from user-generated text. Using data collected from Reddit communities related to sleep and mental health, the proposed approach integrates attention mechanisms, emotion-aware features, and topic modelling to capture nuanced linguistic patterns associated with sleep disturbances and emotional states. Experimental results demonstrate that SleepDepNet outperforms baseline models, achieving F1-scores of 0.89 for sleep quality classification and 0.86 for depressive sentiment analysis. The model's attention mechanisms provide interpretability by highlighting linguistically salient indicators linked to emotional and sleep-related expressions. Additionally, the proposed SleepDepScore, which integrates outputs from both tasks, offers a unified measure for assessing combined risk levels and supporting prioritization in downstream applications. Overall, the findings suggest that multi-task learning offers a promising direction for modelling complex relationships between sleep and mental health in online discourse. While the results demonstrate strong performance under controlled conditions, the framework is designed to support scalable analysis and can inform future research on digital mental health monitoring and early risk identification.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 5","pages":"e0000859"},"PeriodicalIF":7.7,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13152182/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847026","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":"Effectiveness of a universal digital-human parenting intervention in promoting early childhood development and protection: A pragmatic cluster randomized controlled trial.","authors":"Zuyi Fang, Qing Han, Ruochen Ruan, Xinyu Shi, Cheng Zhang, Dongqin Ruan, Xiangming Fang, Inge Vallance, Jamie M Lachman","doi":"10.1371/journal.pdig.0001357","DOIUrl":"10.1371/journal.pdig.0001357","url":null,"abstract":"<p><p>Delayed early childhood development and violence against children are major global challenges, particularly in low-resource settings. Universal digital-human parenting interventions may offer a scalable solution by overcoming barriers associated with traditional in-person programs. This study reports the first pragmatic randomized controlled trial evaluating a blended chatbot-based parenting intervention delivered within the Chinese preschool system. The trial was conducted in a lower-middle-income city in central China. Twenty-one preschool classes were cluster-randomized to a treatment group (n = 10) or waitlist control (n = 11). Primary caregivers of enrolled children participated following informed consent. The intervention comprised a 2.5-month chatbot-led digital parenting program supported by weekly or twice-weekly online group sessions facilitated by headteachers and social workers. Data were collected at baseline, post-intervention, and at 6- and 12-month follow-ups. Primary outcomes were caregiver-provided early learning and stimulation, and caregiver-perpetrated violence. Analyses followed the intention-to-treat principle using multilevel regression models. Equity effects related to caregiver and child disability were explored through moderation and subgroup analyses. Sustainability of impacts was assessed, and complier average causal effects examined the role of intervention completion. Between March 2024 and June 2025, 541 caregivers of children aged 3-6 years were enrolled (treatment: n = 272; control: n = 269), of whom 25.2% were male. Overall, 60.3% completed all chatbot modules. At post-intervention, the program significantly improved early learning and stimulation (β = 1.79, 95% CI [0.24, 3.34]) and reduced caregiver-perpetrated violence (IRR = 0.87, 95% CI [0.80, 0.96]). The intervention showed potential to advance equity for families affected by disability, with some effects sustained at follow-up. Complier analyses indicated reduced endorsement of corporal punishment and lower parental anxiety among participants completing at least 30 modules. Universal digital-human parenting interventions embedded in preschool systems can enhance early childhood development and reduce violence, highlighting the importance of human support and cultural adaptation to optimize engagement and outcomes.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 5","pages":"e0001357"},"PeriodicalIF":7.7,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13152119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846982","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":"The MARC SE-Africa dashboard: Joining forces to counteract emerging antimalarial resistance in South and East Africa.","authors":"Stephanie van Wyk, Ishen Seocharan, Eulambius M Mlugu, Dhol S Ayuen, Donnie Mategula, Tikhala Makhaza, James Kiarie, Victor Asua, Jimmy Opigo, Aimable Mbituyumuremyi, Kibor Kipkemoi Keitany, Emmah Mongina Nyandigisi, Pierre Sinarinzi, Peter Aguek Kon Baak, Tommy Nseka Manbul, Samwel Lazaro Nhiga, Sijenunu Aron Mwaikambo, Maulid Kassim, Sija Joseph Sija, Abdikarin Hussein Hassan, Michael Katende, Jaishree Raman, Karen I Barnes","doi":"10.1371/journal.pdig.0000743","DOIUrl":"10.1371/journal.pdig.0000743","url":null,"abstract":"<p><p>Regions within eastern and southern Africa (SE-Africa) carry some of the highest malaria burdens. Understanding the spatiotemporal dynamics of the emergence and spread of artemisinin (partial) resistance (ART-R) and how to mitigate ART-R is therefore of paramount importance in these areas. Here, we present a dashboard developed by the Mitigating Antimalarial Resistance Consortium for SE-Africa in collaboration with nineteen national control malaria programs (NCMPs) and their partners. The dashboard supports NCMPs' decision-making by providing curated information on the latest available antimalarial resistance data. We systematically reviewed, collated, and visualized antimalarial resistance information from therapeutic efficacy studies, molecular surveillance for Pfkelch13 ART-R genetic markers, current in-country malaria treatment policies, and reported malaria cases and deaths. We identified evidence gaps in therapeutic efficacy and molecular surveillance, particularly in southern Africa. Five countries, Angola, the Democratic Republic of Congo, Kenya, Tanzania and Uganda, reported artemether-lumefantrine treatment failures above the WHO threshold of 10% after correcting for reinfections. The A675V, R561H, P574L, and C469F Pfkelch13 markers were highly prevalent in cross-border regions of several East African countries, with the C469Y marker rapidly spreading across Uganda. The dashboard provides an interactive platform for sharing regional data. We discuss the implications of these findings for policy, practice, and research.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 5","pages":"e0000743"},"PeriodicalIF":7.7,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13148663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846983","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":"Identifying factors associated with vaping cessation in young adults: A machine learning and XAI approach.","authors":"Poolakkad S Satheeshkumar, Ian Lango, Swarnali Zafo, Mikaiel Ebanks, Rahul Kumar Das, Kit Wai Cheung, Roberto Pili, Supriya D Mahajan","doi":"10.1371/journal.pdig.0001031","DOIUrl":"https://doi.org/10.1371/journal.pdig.0001031","url":null,"abstract":"<p><p>The public health impact of vaping in the United States reflects a complex balance of potential benefits and emerging risks, as e‑cigarettes may reduce exposure to toxic combustion byproducts and support adult smoking cessation, yet growing evidence links vaping to respiratory and cardiovascular harm and youth uptake remains concerning, with 38.4% of adolescent users in 2024 reporting habitual use. To inform the optimal use of predictive technologies in cessation efforts, this study sought to characterize cessation‑related behaviors and attitudes among young adult vapers and evaluate machine learning and explainable AI methods for predicting quit attempts and cessation success. A social media-based survey captured behavioral, contextual, and demographic factors, and cessation was defined as self‑reported abstinence from all vaping products for at least 30 days. Predictors were identified using forward selection and backward elimination, and data were split into training and testing sets. Linear models (LASSO, ridge regression, elastic net) and nonlinear models (random forest, support vector machine) were trained and evaluated using AUC and Brier scores. Linear models demonstrated the strongest overall performance: LASSO achieved AUCs of 0.89 (training) and 0.91 (testing), ridge regression 0.88 and 0.93, and elastic net 0.91 for both sets. Nonlinear models showed signs of overfitting, with random forest achieving 0.99 in training but only 0.70 in testing, and SVM achieving 0.89 and 0.72. Key predictors included age, environmental triggers, vaping frequency, sex, and long‑term behavioral outlook. Individuals under 25 showed greater vulnerability to continued use, environmental cues, especially social exposure, were strongly associated with relapse, and erratic vaping patterns predicted lower cessation success. While these models highlight behavioral and contextual factors that may influence cessation, findings should be interpreted as exploratory given the cross‑sectional design and sample characteristics. Larger, longitudinal studies are needed to validate these insights and clarify the potential of predictive modeling to inform targeted public health interventions.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 5","pages":"e0001031"},"PeriodicalIF":7.7,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13143109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846990","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":"Early identification of high-risk individuals for mortality after lung transplantation: A retrospective cohort study with topological feature engineering.","authors":"Alexy Tran-Dinh, Enora Atchade, Sébastien Tanaka, Brice Lortat-Jacob, Yves Castier, Hervé Mal, Jonathan Messika, Pierre Mordant, Philippe Montravers, Ian Morilla","doi":"10.1371/journal.pdig.0001050","DOIUrl":"https://doi.org/10.1371/journal.pdig.0001050","url":null,"abstract":"<p><p>Lung transplantation remains the only definitive treatment for end-stage respiratory failure; however, it has substantial post-operative mortality risk. Current methods like the Lung Transplant Risk Index offer limited predictive performance. This study introduces a novel topological feature engineering model to assess mortality risk. The objective is to improve predictive accuracy by capturing complex temporal patterns while ensuring interpretability. A retrospective cohort study was conducted using clinical data from lung transplant recipients. The model integrates static and time-dependent variables through topological feature extraction, enabling sequential risk updating at transplantation, ICU admission, and throughout early post-operative course. Performance was compared to established methods using a held-out test set. Metrics included accuracy, sensitivity, specificity, and AUC. Interpretability was assessed using Shapley Additive explanations. The proposed model demonstrated superior predictive performance compared to traditional clinical risk scores (LTRI, CCI) and standard machine learning models. On the test dataset, it achieved 87.4% accuracy, 84.1% sensitivity, and 89.6% specificity, with an absolute AUC gain of 0.08 over the best non-topological baseline (p < 0.001). The model consistently outperformed existing approaches across subgroups including age, underlying disease, and transplant type. Shapley analysis revealed that dynamic variables such as early post-operative oxygenation trends, immunosuppressive load, and inflammatory markers were among the most critical contributors to mortality risk. The integration of topological features significantly enhances prediction of post-transplant mortality risk. These findings highlight topological transformers as a valuable tool for precision medicine and clinical decision support.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 5","pages":"e0001050"},"PeriodicalIF":7.7,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13143088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847274","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":"Dynamically predicting renal failure after development of diabetes across biobanks.","authors":"Aubrey Jensen, Sayera Dhaubhadel, Jonathan Hori, Nabil Alami, Gang Li, Hua Zhou, Sridharan Raghavan, Benjamin H McMahon, Peter Reaven, Jin J Zhou","doi":"10.1371/journal.pdig.0001375","DOIUrl":"https://doi.org/10.1371/journal.pdig.0001375","url":null,"abstract":"<p><p>End-stage renal disease (ESRD) remains a major complication of diabetes, yet existing static risk scores may lose accuracy as patient profiles evolve and competing mortality risks change over time. We developed and externally validated a landmark-based ESRD Dynamic Risk Score (ESRD-DRS) that updates individual risk estimates at 1, 5, and 10 years after diabetes diagnosis using routinely collected EHR data. We assembled a retrospective cohort of 708,435 U.S. Veterans with newly diagnosed diabetes in the Veterans Health Administration (VHA). At 1, 5, and 10 years after diabetes diagnosis (LM1, LM5, and LM10), we fit penalized Fine-Gray subdistribution hazard models, drawing from more than 400 demographic, medication, comorbidity, and laboratory variables. Models were evaluated over 1-, 5-, and 10-year horizons for discrimination (area under the time-dependent receiver operating characteristic curve [AUROC]) and calibration (Brier score), and compared with the established RECODe and 4-variable KFRE risk equations. External validation was performed in an independent All of Us (AoU) cohort (n = 13,223). In the VHA cohort (median follow-up 7.6 years), 8,955 patients (1.26%) developed ESRD, and 136,666 (19.3%) died without ESRD. At LM1, ESRD-DRS achieved AUROCs of 0.93, 0.90, and 0.85 for 1-, 5-, and 10-year risk, respectively, and Brier scores ranging from 0.00098 to 0.0162. In the AoU cohort, corresponding AUROCs were 0.94, 0.91, and 0.86, with similar calibration performance. RECODe and KFRE yielded lower discrimination and poorer calibration. Top risk predictors, including estimated glomerular filtration rate, albuminuria, systolic blood pressure, and age, were consistent across landmarks and cohorts. ESRD-DRS, a scalable landmark approach that accounts for competing mortality and evolving patient profiles, outperformed existing static equations. Embedding ESRD-DRS into EHR workflows may support more timely, individualized ESRD risk assessment in patients with diabetes.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 5","pages":"e0001375"},"PeriodicalIF":7.7,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13138643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847312","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":"Exploratory association between multimodal AI-derived digital biomarkers and in-hospital mortality in adult patients with pneumonia: A proof-of-concept study.","authors":"Alejandro Hernández-Arango, Daniel Mejía Arrieta, Christian Andrés Díaz León, Juan G Paniagua Castrillon, Julián Rondón-Carvajal, Melissa Alejandra Acosta, David Restrepo, Wayner Barrios, Santiago Álvarez-López, Jesús Francisco Vargas-Bonilla, Hernán Felipe García Arias, José Julián Garcés Echeverri, Carlos Salazar-Martínez, Olga Lucia Quintero Montoya","doi":"10.1371/journal.pdig.0000960","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000960","url":null,"abstract":"<p><p>Pneumonia remains a leading cause of in-hospital mortality worldwide. Current prognostic tools such as the IDSA/ATS severity score have meaningful limitations, particularly in capturing dynamic disease progression or integrating heterogeneous biological signals. Artificial intelligence (AI) offers the opportunity to derive complementary prognostic information from routinely collected electronic health record data. This exploratory, proof-of-concept retrospective study enrolled adults (≥18 years) admitted with a primary diagnosis of acute pneumonia at Hospital Alma Máter de Antioquia (Medellín, Colombia) between January 1 and June 30, 2024. After applying pre-defined exclusion criteria, 121 patients (19 non-survivors, 15.7%) comprised the final analytic cohort. Three independent AI modules were applied: (i) a ResNet-18 deep learning model quantified lung consolidation from chest radiographs (CXRs) using Class Activation Mapping; (ii) a Spanish regular-expression natural language processing (NLP) pipeline extracted modified IDSA/ATS severity scores from clinical notes; and (iii) NeuroKit2-based quantitative heart rate variability (HRV) analysis processed electrocardiogram (ECG) signals digitised from PDF archives. Bivariate associations with all-cause in-hospital mortality were examined using logistic regression. Several features exhibited statistically significant associations with mortality under conventional thresholds: AI-quantified total lung compromise ratio (OR 8.32, 95% CI 1.23-56.29), NLP-derived IDSA/ATS severity score (OR 1.78, 95% CI 1.09-2.88), and a coherent ECG/HRV profile characterised by higher heart rate (120.1 vs. 84.4 bpm, p = 0.023), reduced RMSSD (4.1 vs. 23.5 ms, p = 0.041), reduced Poincaré SD1 (3.0 vs. 17.6 ms, p = 0.041), and T-wave amplitude reductions surviving FDR correction in multiple leads. Given the small sample size and low event count (n = 19; events-per-variable ≈ 4), all associations are preliminary and hypothesis-generating only. These proof-of-concept findings suggest that integrated multimodal AI biomarkers automatically derived from low-resource clinical data can capture a cardiopulmonary stress profile associated with pneumonia mortality, and support the design of larger prospective validation studies.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 4","pages":"e0000960"},"PeriodicalIF":7.7,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13132438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824475","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":"Co-designing eCap-CoDe: A mobile health application for primary health care-based dementia care in rural Uganda.","authors":"Edith K Wakida, Christine K Karungi, William Wasswa, Recho Katusabe Ajok, Godfrey Z Rukundo, Ou Zhang, Alexandra Lopez-Vera, Zohray M Talib, Celestino Obua","doi":"10.1371/journal.pdig.0001389","DOIUrl":"https://doi.org/10.1371/journal.pdig.0001389","url":null,"abstract":"<p><p>Dementia is an emerging public health challenge in low- and middle-income countries (LMICs), yet it remains underdiagnosed in rural Uganda, where primary health care (PHC) providers often lack tools, training, and data systems for early detection and management. Mobile health (mHealth) applications can enhance provider capacity, improve data capture, and strengthen feedback systems. This study explored the perspectives of PHC providers and District Health Teams (DHTs) to inform the co-design of eCap-CoDe, a mobile application for community-based dementia care in rural Uganda. We conducted in-depth interviews with 31 participants from two rural districts: 23 PHC providers (medical/clinical officers and nurses) and 8 DHT members. Participants were purposively sampled for diversity in cadre, experience, and facility type. Data were thematically analyzed using the Consolidated Framework for Implementation Research (CFIR), with four a priori domains, i.e., content, user experience, organizational, and service delivery, guiding coding and analysis. Content requirements - included dementia-specific screening and management tools, modular in-app training aligned with the WHO mhGAP Intervention Guide, and structured data capture integrated with Uganda's Health Management Information System (HMIS). User experience needs: emphasized simple, intuitive interfaces with dropdown menus, checkboxes, audio-visual decision support, and offline functionality to address connectivity gaps. Organizational requirements: prioritized interoperability with District Health Information System 2 (DHIS2), integration with supervisory workflows, and dementia-specific performance indicators. Service delivery needs: focused on real-time feedback loops, reducing duplicate documentation, and potential expansion to other common conditions to enhance utility and uptake. Co-designing mHealth tools with end-users ensures alignment with the realities of workflows, systems, and infrastructure. eCap-CoDe addresses capacity, data, and feedback gaps in rural dementia care and offers a scalable model for integrating digital tools into PHC in Uganda and similar LMICs. Pilot testing will assess the feasibility, usability, and impact before scaling up.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 4","pages":"e0001389"},"PeriodicalIF":7.7,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13132442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824448","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":"Accuracy of heart rate measurement using AirPods Pro 3 during graded treadmill exercise: A laboratory-based validation study.","authors":"Cailbhe Doherty, David Burke, Owen Mitchell, David Lawless, Jasman Brar, Michael Fuchs, Rory Lambe","doi":"10.1371/journal.pdig.0001373","DOIUrl":"https://doi.org/10.1371/journal.pdig.0001373","url":null,"abstract":"<p><p>AirPods Pro 3 incorporate an in-ear optical heart-rate sensor, but independent validation during exercise is limited. We evaluated agreement between AirPods Pro 3 heart rate and an ECG-derived chest-strap reference (Polar H10) during graded treadmill exercise in a controlled laboratory setting. Forty adults (mean age 23·8 years; 37·5% female) completed a protocol comprising rest and progressive exercise stages targeting ~40-85% of age-predicted maximal heart rate, including rapid workload transitions. Heart-rate time series from both devices were synchronised by timestamp and aggregated into non-overlapping 5-s epochs. Agreement was assessed using a repeated-measures Bland-Altman approach implemented via a linear mixed-effects model with participant-level random effects; absolute error metrics were calculated at the participant level and summarised overall and by intensity category. Across 16,735 paired epochs, mean bias was -0·03 beats·min ⁻ ¹ (AirPods Pro 3 minus Polar H10; 95% CI -0·22 to 0·17), indicating negligible systematic error. The total standard deviation of differences was 5·23 beats·min ⁻ ¹, yielding 95% limits of agreement from -10·27 to 10·22 beats·min ⁻ ¹, with greater dispersion at higher heart rates. Overall mean absolute error was 2·08 beats·min ⁻ ¹ and mean absolute percentage error was 2·02%, with mean absolute error ranging from 1·31 to 2·4 beats·min ⁻ ¹ across intensity categories. AirPods Pro 3 therefore provided heart-rate estimates closely aligned with a validated chest-worn reference during graded treadmill exercise in healthy adults, with minimal bias and low average error but wider epoch-to-epoch variability at higher intensities.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 4","pages":"e0001373"},"PeriodicalIF":7.7,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13132241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824505","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}