PLOS digital health最新文献

Machine learning (ML) models are increasingly applied to predict body mass index (BMI) and related outcomes, yet their fairness across socioeconomic and caste groups remains uncertain, particularly in contexts of structural inequality. Using nationally representative data from more than 55,000 adults aged 45 years and older in the Longitudinal Ageing Study in India (LASI), we evaluated the accuracy and fairness of multiple ML algorithms-including Random Forest, XGBoost, Gradient Boosting, LightGBM, Deep Neural Networks, and Deep Cross Networks-alongside logistic regression for predicting underweight, overweight, and central adiposity. Models were trained on 80% of the data and tested on 20%, with performance assessed using AUROC, accuracy, sensitivity, specificity, and precision. Fairness was evaluated through subgroup analyses across socioeconomic and caste groups and equity-based metrics such as Equalized Odds and Demographic Parity. Feature importance was examined using SHAP values, and bias-mitigation methods were implemented at pre-processing, in-processing, and post-processing stages. Tree-based models, particularly LightGBM and Gradient Boosting, achieved the highest AUROC values (0.79-0.84). Incorporating socioeconomic and health-related variables improved prediction, but fairness gaps persisted: performance declined for scheduled tribes and lower socioeconomic groups. SHAP analyses identified grip strength, gender, and residence as key drivers of prediction differences. Among mitigation strategies, Reject Option Classification and Equalized Odds Post-processing moderately reduced subgroup disparities but sometimes decreased overall performance, whereas other approaches yielded minimal gains. ML models can effectively predict obesity and adiposity risk in India, but addressing bias is essential for equitable application. Continued refinement of fairness-aware ML methods is needed to support inclusive and effective public-health decision-making.

Online/digital health literacy is important for individuals to evaluate the influence of such input in their care and consent for treatment. The purpose of this systematic review is to examine the digital health literacy level among adults in studies that used the eHealth Literacy Scale (eHEALS) as a measure of digital health literacy. The authors searched Google Scholar, PubMed, Scopus, and Web of Science for evidence following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Statement, 2020 (PRISMA). Included were articles in which the researchers evaluated the level of digital health literacy using eHEALS, were peer reviewed, written in English or in which English translation was provided, and were published between 2020-2025. There were 200 articles initially identified in the search, 180 were excluded resulting in a sample of 20 publications. EHEALS scores, with possibilities from 8-40, had a weighted mean of 24.3 (95%CI: 17.1-31.6). The lowest mean score was 12.57; and the highest mean score was 35.1. The highest eHEALS score was from a qualitative interview study. Nine other studies reported overall means ≥ 30. There were three with eHEALS scores below 20. Globally, there is a wide range of reported digital health literacy levels. It is critical that the public gains skill and confidence in digital health literacy for healthcare decisions. The results of this study provide evidence of a large range of digital health literacy.

In the healthcare domain, images play a pivotal role in clinical diagnoses, treatment planning, surgical procedures, and epidemiological insights. Nevertheless, challenges such as limited experience among healthcare professionals, risk of misdiagnosis and subjective interpretation, and factors like stress and fatigue may jeopardize the precision with which patients are assessed. In this regard, professionals in the field of Dentistry face analogous challenges given that distinguishing anatomical structures in dental imaging requires expert interpretation and precise analysis. Convolutional Neural Networks (CNNs) offer promising opportunities to analyze images during patient care and can enhance diagnostic accuracy and clinical decision-making, benefiting both patients and healthcare providers. Here, we aimed to develop a specialized analyzer for digital dental radiography, that focuses on numbering teeth and detecting tooth cavities. The system is designed to achieve high precision, recall, accuracy, specificity, and F1-score, to ensure that diagnosis is reliable and accurate. In this study, we specifically explore Inception-v3 and InceptionResNet-v2 to discern cavitated teeth and tooth positions in dental panoramic radiographic images (PANs). On the basis of 935 PANs sourced from routine patient care, annotated by dentists at the Faculty of Dentistry of Ribeirão Preto in Brazil, our approach achieved precision of 0.98, recall of 0.98, accuracy of 0.998, specificity of 0.999 and F1-score of 0.98 for tooth numbering. Concerning identification of cavitated teeth, our approach reached precision of 0.96, recall of 0.91, accuracy of 0.94, specificity of 0.96 and F1-score of 0.94. By addressing the critical challenges and reaching high performance, our study serves as a benchmark that relates innovative research and real-world applications, fostering advancements in dental diagnosis. The performance reported herein demonstrates that our initiatives can modulate image analysis tasks and select a more suitable CNN for the job.

Despite the millions of individuals struggling with persistent symptoms, Long COVID has remained difficult to diagnose due to limited objective biomarkers, often leading to underdiagnosis or even misdiagnosis. To bridge this gap, we investigated the potential of utilizing wearable sensor data to aid in the diagnosis of Long COVID. We analyzed longitudinal heart rate (HR) data from 126 individuals with acute SARS-CoV-2 infections to develop machine learning models capable of predicting Long COVID status using derived HR features, symptom features, or a combination of both feature sets. The HR features were derived across six analytical categories, including time-domain, Poincaré nonlinear, raw signal, Kullback-Leibler (KL) divergence, variational mode decomposition (VMD), and the Shannon energy envelope (SEE), enabling the capture of heart rate dynamics over various temporal scales and the quantification of day-to-day shifts in HR distributions. The symptom features used in the final models included chest pain, vomiting, excessive sweating, memory loss, brain fog, heart palpitations, and loss of smell. The combined HR- and symptom-feature model demonstrated robust predictive performance, achieving an area under the Receiver Operating Characteristic curve (ROC-AUC) of 95.1% and an area under the Precision-Recall curve (PR-AUC) of 85.9%. These values represent a significant improvement of approximately 5% in both the ROC-AUC and PR-AUC over the symptoms-only model. At the population level, this improvement in discrimination could lead to clinically meaningful reductions in misclassification and improved patient outcomes, achieved through a non-invasive diagnostic tool. These findings suggest that wearable HR data could be used to derive an objective biomarker for Long COVID, thereby enhancing diagnostic precision.

Induction of labor (IOL) is a common yet complex clinical procedure associated with varying risks, including cesarean section (CS). Accurate prediction models may help support more informed, personalized decision-making. This study aimed to develop and validate an explainable machine learning prediction model for CS following IOL. We used population-based administrative perinatal datasets from two Australian states (New South Wales (NSW) and Queensland) covering all births between 2016 and 2019 for model development. Temporal validation was conducted using 2020 births from NSW, and geographical validation using 2016-2018 births from Victoria. We included women with singleton, cephalic, term, live births who attempted IOL and had no prior CS. Seven models (logistic regression, random forest, gradient boosting, LightGBM, XGBoost, CatBoost, and AdaBoost) were developed with hyperparameter tuning and feature selection. Performance was assessed using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, calibration plot (overall and across sociodemographic subgroups), decision curve analysis, Brier Score, and model parsimony. SHAP (SHapley Additive exPlanations) values were used to explain predictor contributions. A total of 180,700 women were included in model development (mean age 31 ± 5 years; CS = 20.8%). The optimal model, developed using XGBoost with ten predictors, achieved AUROCs of 0.76 (95% CI: 0.75-0.77) and 0.75 (95% CI: 0.74-0.76) in temporal (n = 14,527; CS = 22.5%) and geographical (n = 14,755; CS = 19.0%) validations, respectively. The most influential predictors were nulliparity, pre-pregnancy body mass index, and maternal age, while diabetes and hypertension (pre-existing or pregnancy-related) contributed least. Women with higher predicted CS probabilities had increased inpatient costs and maternal morbidity, regardless of actual mode of birth. The final model is accessible via an interactive web application (https://csai-8ccf2690242c.herokuapp.com/). This model demonstrates strong predictive performance using routinely collected maternal factors. Further co-design and implementation research is needed before potential clinical adoption.

Chronic disease management is a burden for many patients. Digital health tools (DHTs) can leverage technology to rapidly develop and disseminate interventions to alleviate obstacles faced and promote self-care. Primary care physicians (PCPs) are most directly involved in the care of chronic disease patients; however, their perspective is often overlooked. To develop an effective DHT for chronic disease management, PCP attitudes are critical to ensure improved patient integration, adoption and care outcomes. The purpose of this rapid review is to explore and identify PCPs' perspectives and attitudes regarding DHTs for chronic disease management and generate major themes from our findings using key literature. The themes will be used to guide DHT creators, clinicians and policy makers on adoption and implementation considerations. We conducted a rapid review of primary qualitative research between 2000 and 2022. Two reviewers, independently, conducted study screening, selection, and data abstraction. The themes identified in the articles were extracted and presented narratively. The data was analyzed using NVIVO12 software. Braun and Clarke's deductive thematic analysis was used, and the themes identified were extracted and presented narratively. Nine qualitative research studies met the inclusion criteria. Themes were classified into two major categories: physician-patient relationship and physician-technology relationship. Within these, seven subcategories were identified: (1) Increased Physician Workload, (2) Data Capture & Data Quality, (3) Evidence-Based Care, (4) Education and Training, (5) Liability, (6) Patient Interactions, and (7) Patient Empowerment and Suitability. DHT creators/endorsers need to consider how DHTs affect the patient-physician relationship and the physician-technology relationship as this affects how PCPs perceive DHTs. PCPs' perspectives must be taken into consideration to promote self-care for patients living with chronic diseases.

Most COVID-19 exposure-notification apps still use binary contact tracing (BCT): once a test is positive, every contact whose accumulated risk exceeds a fixed threshold receives the same quarantine order. Because those alerts are late and blunt, BCT can miss early spread while triggering mass isolation. We propose Network-based Proactive Contact Tracing (NPCT), a privacy-preserving, fully decentralized intervention scheme that can run on existing exposure-notification infrastructure. Each user's recent Bluetooth contact history is condensed into an individual risk score and compared against a dynamic, epidemic-aware threshold controlled by a single global sensitivity parameter. Crossing that threshold triggers a graded "reduce contacts by X%" prompt rather than an all-or-nothing quarantine. Simulations on four synthetic and empirical temporal networks show that NPCT can cut the epidemic peak by ≈ 40% while suppressing only 20% of contacts. The intervention burden concentrates on the highest-risk individuals, and the scheme's qualitative behavior remains stable across network types, horizons, and compliance levels. These properties make NPCT a practical upgrade path for national BCT apps, balancing epidemic control with privacy protection and social cost.

Community health systems are poised to play a prominent role in achieving universal health coverage in low- and middle-income countries, as demonstrated during the COVID-19 pandemic response. The advent of health information technology has provided an opportunity to optimize the community health space and improve efficiency. However, there is limited knowledge about the acceptance and usage of health information technology among community health workers, a prerequisite for scaled implementation. This study aimed to use the technology acceptance model (TAM) to predict the acceptance and usage of health information technology among CHWs, identify external factors, and understand the impact on community health systems. Specifically, we conducted semi-structured interviews with 170 community health workers who were recruited through both convenience and snowball sampling. We then performed response coding and cross-tabulation, correlation, and regression analysis. As a result, the TAM effectively predicted CHWs' behavioral intention to use digital health tools. However, actual usage was not well predicted, and there was a mismatch between high behavioral intention and low actual usage. Access to smartphones emerged as a major determinant of actual usage, overshadowing other variables in the TAM. In conclusion, while CHWs show strong acceptance of digital health tools, structural barriers, particularly limited access to smartphones, hinder their actual use. These findings highlight the importance of addressing infrastructural inequities to enable the effective and equitable digitization of community health systems.

The global shift toward digital health communication presents both opportunities and challenges for older adults, whose populations is expanding rapidly. This study explored how older adults and health content producers engage with health information across paper and digital formats, and assessed the potential of hybrid approaches such as augmented paper. Two qualitative studies were conducted in Surrey, UK: focus groups with older adults (n = 9) and interviews with public health professionals (n = 6). Data were analysed through content and thematic analysis to identify user requirements. Findings show that older adults continue to value printed materials for familiarity and reliability, but turn to digital formats for timeliness and convenience. Trust in online content, ease of use, and device compatibility emerged as central concerns shaping engagement. Content producers echoed these challenges, highlighting cost constraints and the need for accessible, multi-format materials. Both stakeholder groups favoured app-free connections between print and digital content, with QR codes preferred for their simplicity, familiarity, and avoidance of app installation. Participants also emphasised the importance of multimodal presentation (e.g., text, video, audio) and options to self-print key materials. While based on a small, UK-specific sample, the study highlights design implications for inclusive health communication. Hybrid solutions that combine print with carefully curated digital resources can reduce barriers linked to trust and usability, and extend access for older adults with varied levels of digital confidence. These insights provide actionable guidance for public health organisations and policymakers seeking to balance cost-effectiveness with accessibility. Broader testing in more diverse populations is recommended to refine these strategies and ensure equitable health communication worldwide. These findings underline the importance of designing hybrid health communication strategies that are not only user-friendly but also equitable, supporting the goals of the WHO Decade of Healthy Ageing by promoting inclusive access to reliable health information for older adults worldwide.