PLOS digital health最新文献

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.

[This corrects the article DOI: 10.1371/journal.pdig.0000310.].

Continuous glucose monitoring (CGM) in healthy adults is becoming part of healthy lifestyle activities for preventing cardio-vascular and metabolic diseases. However, there is a lack in describing individual glucose responses to everyday situations, with appropriate metrics. The aim of this study was to provide metrics which describe individual glucose responses to lifestyle factors including diet, exercise, and stress in healthy, young adults. Ten participants wore a CGM device (FreeStyle Libre3®) for 14 consecutive days while completing nine standardized interventions (challenges) consisting of food, anaerobic and aerobic sport, and the Trier Social Stress Test (TSST) in an exploratory, clinical trial. Individual glucose responses after each challenge were assessed over four hours, using the following metrics: AUC0-4, max glucose, time to max glucose, glucose excursion, and time required for glucose levels to return to baseline (Glucose Recovery Time to Baseline (GRTB)). The study has been registered in the German clinical trial registry (Study number: DRKS00032821). Anaerobic exercise resulted in a significantly greater glucose excursion (28.7 ± 21.46 mg/dL) compared to aerobic exercise (8.8 ± 4.91 mg/dL, p = 0.0228). Food with a rich carbohydrate content caused the highest glucose increase (161.4 ± 15.59 mg/dL). TSST resulted in a significant difference in baseline-corrected glucose concentrations over time as revealed by a two-factor repeated measures ANOVA (p = 0.0113). We provide reference data of glucose response to lifestyle factors such as diet and exercise in healthy adults. Psychobiological stress revealed a substantial glucose response. Using GRTB metrics may quantify the lifestyle stimulus on the important metabolic pathway and can be utilized alongside kinetic metrics for describing individual glucose responses.

Ensuring privacy in distributed machine learning while computing the Area Under the Curve (AUC) is a significant challenge because pooling sensitive test data is often not allowed. Although cryptographic methods can address some of these concerns, they may compromise either scalability or accuracy. In this paper, we present two privacy-preserving solutions for secure AUC computation across multiple institutions: (1) an exact global AUC method that handles ties in prediction scores and scales linearly with the number of samples, and (2) an approximation method that substantially reduces runtime while maintaining acceptable accuracy. Our protocols leverage a combination of homomorphic encryption (modified Paillier), symmetric and asymmetric cryptography, and randomized encoding to preserve the confidentiality of true labels and model predictions. We integrate these methods into the Personal Health Train (PHT)-meDIC platform, a distributed machine learning environment designed for healthcare, to demonstrate their correctness and feasibility. Results using both real-world and synthetic datasets confirm the accuracy of our approach: the exact method computes the true AUC without revealing private inputs, and the approximation provides a balanced trade-off between computational efficiency and precision. All relevant code and data is publicly available at https://github.com/PHT-meDIC/PP-AUC, facilitating straightforward adoption and further development within broader distributed learning ecosystems.

Black women are disproportionally likely to contract sexually transmitted infections (STIs) including HIV compared to women of other races and ethnicities. It is possible that mobile health (referred to as "mHealth") strategies, including mobile applications, designed for Black women could provide sexual health support and reduce STI/HIV transmission. We sought to explore acceptability of mHealth strategies among Black women and to identify if preferences varied by age or HIV vulnerability. We surveyed 213 Black women aged 14-64 attending a family planning clinic in Chicago. We asked about mHealth use, desired sources of sexual health information, and mHealth application (app) features. Responses were analyzed as dichotomous variables, with age categorized as ≤24 years of age or ≥25 years of age and HIV vulnerability score categorized as low (<2) or high (≥2). HIV vulnerability was determined based on affirmative answers to the following questions: having had condomless sex (either vaginal or anal) in the past three months, having had an abortion in the past 12 months, having received STI treatment in the past three months, and having had ≥ 2 sex partners in the last three months. Odds ratios and 95% confidence intervals (OR 95% CI) were created using logistic regression models. The majority of participants were interested in using technology as part of their sexual health care (84.5%) and were likely to download an mHealth app (74.7%). Many questions about desirability and interest in app features did not differ by age or HIV vulnerability category. Black women ≥25 years had 7.3 times the odds of rating the inclusion of short videos as an important part of the mHealth app (OR 7.3 95% CI (1.7, 32.4)). Within this population, interest in using a sexual health app was high, suggesting an openness to app development for both sexual health as well as specifically for pre-exposure prophylaxis.