PLOS digital health最新文献

{"title":"What influences engagement with a bipolar disorder self-management app? A qualitative investigation of use of the PolarUs app.","authors":"Emma Morton, Rachelle Hole, Heather O'Brien, Linda Li, Steven J Barnes, Erin E Michalak","doi":"10.1371/journal.pdig.0001017","DOIUrl":"https://doi.org/10.1371/journal.pdig.0001017","url":null,"abstract":"<p><p>Interventions delivered via smartphone apps may support individuals with bipolar disorder (BD) to learn about and implement evidence-based self-management strategies in the context of their daily lives. However, app usage rates are often suboptimal. The subjective experience of users may provide insights into factors influencing engagement (and disengagement) with an mHealth intervention. The present study describes a qualitative investigation of the experiences of people with BD who participated in the evaluation of a novel app-based intervention for BD self-management, the PolarUs app. Twenty-five individuals with BD were provided with access to an app-based self-management intervention over a three-month study period, and were later interviewed about personal experiences of engagement with the intervention, including attempts to enact self-management strategies. Thematic analysis was used to identify important aspects of the experience of engaging with a self-management app. Three themes describing drivers of engagement with the PolarUs app and associated features were generated: 1) Motivations, 2) Salience, and 3) Perceived effort. Drivers of engagement were shaped by contextual influences, summarised in four themes: 1) The smartphone ecosystem, 2) Daily life, 3) Mood symptoms, and 4) Involvement in a research study. The findings of this research generate insights into how individuals with BD engage with app-based interventions. Lived experience perspectives can inform the design of engaging app-based interventions for BD. Further, these findings emphasise the importance of considering the context in which people use self-management apps for BD for both research studies and implementation.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 10","pages":"e0001017"},"PeriodicalIF":7.7,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12513578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276871","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":"Collaborative intelligence in AI: Evaluating the performance of a council of AIs on the USMLE.","authors":"Yahya Shaikh, Zainab Asiyah Jeelani-Shaikh, Muzamillah Mushtaq Jeelani, Aamir Javaid, Tauhid Mahmud, Shiv Gaglani, Michael Christopher Gibbons, Minahil Cheema, Amanda Cross, Denisa Livingston, Morgan Cheatham, Elahe Nezami, Ronald Dixon, Ashwini Niranjan-Azadi, Saad Zafar, Zishan Siddiqui","doi":"10.1371/journal.pdig.0000787","DOIUrl":"10.1371/journal.pdig.0000787","url":null,"abstract":"<p><p>The stochastic nature of next-token generation and resulting response variability in Large Language Models (LLMs) outputs pose challenges in ensuring consistency and accuracy on knowledge assessments. This study introduces a novel multi-agent framework, referred to as a \"Council of AIs\", to enhance LLM performance through collaborative decision-making. The Council consists of multiple GPT-4 instances that iteratively discuss and reach consensus on answers facilitated by a designated \"Facilitator AI.\" This methodology was applied to 325 United States Medical Licensing Exam (USMLE) questions across all three exam stages: Step 1, focusing on biomedical sciences; Step 2 evaluating clinical knowledge (CK); and Step 3, evaluating readiness for independent medical practice. The Council achieved consensus that were correct 97%, 93%, and 94% of the time for Step 1, Step 2 CK, and Step 3, respectively, outperforming single-instance GPT-4 models. In cases where there wasn't an initial unanimous response, the Council deliberations achieved a consensus that was the correct answer 83% of the time, with the Council correcting over half (53%) of the responses that majority vote had gotten incorrect. The odds of a majority voting response changing from incorrect to correct were 5 (95% CI: 1.1, 22.8) times higher than the odds of changing from correct to incorrect after discussion. This study provides the first evidence that the semantic entropy of the response space can consistently be reduced to zero-demonstrated here through Council deliberation, and suggesting the possibility of other mechanisms to achieve the same outcome.. This study revealed that in a Council model, response variability, often considered a limitation, can be transformed into a strength that supports adaptive reasoning and collaborative refinement of answers. These findings suggest new paradigms for AI implementation and reveal the heightened strength that emerges when AIs begin to collaborate as a collective rather than operate alone.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 10","pages":"e0000787"},"PeriodicalIF":7.7,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12510544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260240","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":"Strategies for engaging \"hard-to-reach\" populations in a panel for digital health research: A qualitative study among experts.","authors":"Corine Oldhoff-Nuijsink, Mirjam P Fransen, Linda W Peute, Marloes E Derksen","doi":"10.1371/journal.pdig.0001033","DOIUrl":"10.1371/journal.pdig.0001033","url":null,"abstract":"<p><p>Digital health technologies are developed to aid individuals in managing their health. Nonetheless, a significant number of these technologies remain neither implemented nor utilized by potential end users. One contributing factor to this gap in uptake is the insufficient consideration of the target audience needs and requirements during the development phase of these technologies. Moreover, certain groups in society are often underrepresented in such research projects (so called \"hard-to-reach\"), leading to a disconnect between the developed technologies and their needs and requirements. However, recruiting a representative study population - including individuals from different demographic backgrounds - for such studies poses challenges for researchers. One proposed solution is panel research, wherein a fixed group of participants is willing to participate in multiple research projects over time. In this study, we conducted semi-structured interviews with twelve experts in panel management or with researchers working with individuals in a vulnerable position, to gain insights into their experiences. Through thematic analysis, four key themes emerged: diverse recruitment strategies, investment in sustainable participation, simplified informed consent, and regulating practical matters. Recruiting a representative study population requires diverse and active strategies, such as visiting community centres and leveraging key figures. Long-term engagement can be maintained through regular, accessible communication, flexible participation options, and aligning research goals with participants' interests. Additionally, clear expectations, a supportive environment, respect for privacy, and feedback and incentives are crucial for retaining panel members. Taken into account these factors support inclusiveness in digital health research. Ultimately resulting in better alignment between users' needs and the development, implementation and adoption of digital health technologies.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 10","pages":"e0001033"},"PeriodicalIF":7.7,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12510573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260247","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":"Empowering patients through clinical notes access: Opportunities, challenges, and the path forward.","authors":"Yoko Yoshimura, Geva Greenfield, Benedict Hayhoe, Azeem Majeed, Ana Luisa Neves","doi":"10.1371/journal.pdig.0000993","DOIUrl":"10.1371/journal.pdig.0000993","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 10","pages":"e0000993"},"PeriodicalIF":7.7,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12507306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253972","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 lightweight deep neural network for personalized detecting ventricular arrhythmias from a single-lead ECG device.","authors":"Zhejun Sun, Wenrui Zhang, Yuxi Zhou, Shijia Geng, Deyun Zhang, Jiaze Wang, Bin Liu, Zhaoji Fu, Linlin Zheng, Chenyang Jiang, Guigang Zhang, Shenda Hong","doi":"10.1371/journal.pdig.0001037","DOIUrl":"10.1371/journal.pdig.0001037","url":null,"abstract":"<p><p>Ventricular arrhythmia (VA) is a leading cause of sudden cardiac death. Detecting VA from electrocardiograms (ECGs) using deep learning techniques has potential to improve clinical outcomes. However, developing robust deep learning models for ECG analysis remains challenging due to: (1) inter-subject diversity among different individuals, and (2) intra-subject diversity within the same subject across different physiological state over time. In this study, we address these challenges by introducing enhancements in both the pre-training and fine-tuning stages. In the pre-training stage, we propose a novel approach combining model-agnostic meta-learning (MAML) with curriculum learning (CL) to effectively address inter-subject diversity. MAML efficiently transfer knowledge from large-scale datasets and enables rapid model adaptation to new individuals using limited records. Integrating CL further enhances the effectiveness of MAML by sequentially training models from simpler to more complex tasks. For the fine-tuning stage, we propose an improved pre-fine-tuning strategy specifically designed to manage the intra-subject diversity. We evaluate our methods on three publicly available ECG datasets and one real-world clinical ECG dataset collected using a portable device. Our proposed method achieves ROC-AUC = 0.984 / F1 = 0.940 with only 10 beats per class per subject on the test set and also achieves ROC-AUC = 0.965 / F1 = 0.937 on a real-world clinical collected data. Experimental results demonstrate that our proposed approach outperforms existing comparative methods across all evaluation metrics, and have a tendency to address intra-subject diversity. Ablation studies confirm that the combination of MAML and CL leads to more uniform performance across individuals, and our enhanced pre-fine-tuning technique substantially improves model adaptation to individual-specific data.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 10","pages":"e0001037"},"PeriodicalIF":7.7,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12507228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253953","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 sleep homeostatic response to naturalistic sleep loss.","authors":"Balaji Goparaju, Sharon Ravindran, Matt T Bianchi","doi":"10.1371/journal.pdig.0001021","DOIUrl":"10.1371/journal.pdig.0001021","url":null,"abstract":"<p><p>Investigations of Deep sleep homeostasis, the process by which the amount of Deep sleep is increased following a night of reduced sleep, often involve controlled intentional sleep deprivation experiments in service of understanding mechanistic physiology. We tested the hypothesis that a homeostatic increase in Deep sleep is detectable after relative sleep loss arising in naturalistic settings. In this retrospective observational study, we analyzed participants who provided informed consent to participate in the Apple Heart and Movement Study and elected to contribute sleep data (n = 44,564). Instances of relative sleep loss, defined as >=2 hours below each participant's median duration, occurred in 92.9% of participants, most often in isolation, and with a median duration of just over 4 hours. The Deep sleep rebound was proportional to the amount of sleep loss, for short night definitions ranging from 30 minutes to >=3 hours less. Focusing on short nights that were at least 2 hours below the median duration, 58.8% of participants showed any increase in subsequent Deep sleep, with a median increase of 12% (absolute increase of 5 minutes). In addition, the variability in Deep sleep after short nights markedly increased in a dose response manner. The Deep sleep homeostatic response showed little correlation to sleep duration, timing, consistency, or sleep stages, but was inversely correlated with Deep sleep latency (Spearman R = -0.28), another proxy for homeostatic response to sleep loss. The results provide evidence for homeostatic responses in a real-world setting. Although the Deep sleep responses to sleep loss are modest, naturalistic short nights are a milder perturbation compared to experimental sleep deprivation, and reactive behaviors potentially impacting sleep physiology are uncontrolled, leading to wide variance. The findings illustrate the utility of longitudinal sleep tracking to assess real-world correlates of sleep phenomenology established in controlled experimental settings.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 10","pages":"e0001021"},"PeriodicalIF":7.7,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240597","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 case study on barriers to the research implementation of a novel technology in an academic medical center.","authors":"Mechelle Sanders, Kevin Fiscella, Jack Chang, Alain LeBlanc, Peter Veazie","doi":"10.1371/journal.pdig.0001014","DOIUrl":"10.1371/journal.pdig.0001014","url":null,"abstract":"<p><p>Natural Language Processing allows extracting unstructured text data from electronic health records (EHR), but historically required extensive coding and expertise. Amazon Comprehend Medical (ACM) offers a scalable solution for mining EHR data without extensive natural language processing expertise. This case study examined barriers and facilitators to implementing ACM in an academic medical center. We reviewed correspondence regarding ACM implementation between study investigators and respective experts within the medical center. We qualitatively coded the correspondence for barriers and facilitators using the Consolidated Framework for Implementation Research (CFIR) framework as a guide. Key findings included the involvement of non-traditional stakeholders in the approval process and unexpected limitations of anticipated facilitators. The study revealed that implementing novel technologies like ACM in academic medical settings requires careful consideration of safety protocols, which may slow adoption. Our findings can guide research teams in navigating the implementation of similar technologies, balancing innovation with necessary safeguards.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 10","pages":"e0001014"},"PeriodicalIF":7.7,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12494264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145226333","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":"Expression of Concern: Impact of digital wound care solution on healing time: A descriptive study in home health settings.","authors":"","doi":"10.1371/journal.pdig.0001025","DOIUrl":"10.1371/journal.pdig.0001025","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 9","pages":"e0001025"},"PeriodicalIF":7.7,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202409","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":"Self-contrastive weakly supervised learning framework for prognostic prediction using whole slide images.","authors":"Saul Fuster, Farbod Khoraminia, Julio Silva-Rodríguez, Umay Kiraz, Geert J L H van Leenders, Trygve Eftestøl, Valery Naranjo, Emiel A M Janssen, Tahlita C M Zuiverloon, Kjersti Engan","doi":"10.1371/journal.pdig.0000972","DOIUrl":"10.1371/journal.pdig.0000972","url":null,"abstract":"<p><p>We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated prognostic prediction. Prognostic prediction poses a unique challenge as the ground truth labels are inherently weak, and the model must anticipate future events that are not directly observable in the image. To address this challenge, we propose a novel three-part framework comprising of a convolutional network based tissue segmentation algorithm for region of interest delineation, a contrastive learning module for feature extraction, and a nested multiple instance learning classification module. Our study explores the significance of various regions of interest within the histopathological slides and exploits diverse learning methods in real-world clinical scenarios. The pipeline is initially validated on artificially generated data and a simpler diagnostic task. Transitioning to prognostic prediction, tasks become more challenging. Employing bladder cancer as use case, our best models yield an AUC of 0.721 and 0.678 for recurrence and treatment outcome prediction respectively for a private data cohort. Altogether, this research serves as an initial investigation on the shortcomings of histopathological image analysis for treatment outcome prediction.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 9","pages":"e0000972"},"PeriodicalIF":7.7,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202386","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":"Waldo: Automated discovery of adverse events from unstructured self reports.","authors":"Karan S Desai, Vijay M Tiyyala, Pranav Tiyyala, Atharva Yeola, Alejandra Gallegos-Rangel, Alejandro Montiel-Torres, Matthew R Allen, Mark Dredze, Ryan G Vandrey, Johannes Thrul, Eric C Leas, Mike Hogarth, Davey M Smith, John W Ayers","doi":"10.1371/journal.pdig.0001011","DOIUrl":"10.1371/journal.pdig.0001011","url":null,"abstract":"<p><p>Adverse event (AE) detection is labor-intensive and costly given the task is to find rare events. Automated solutions to enhance efficiency, reduce costs, and capture unnoticed safety signals are needed. To develop and evaluate an automated machine learning tool, \"Waldo,\" for AE detection from unstructured social media text data, specifically targeting consumer health products that lack traditional post-market surveillance channels. We tested three models - (i) N-gram model, (ii) BERT (Bidirectional Encoder Representations from Transformers), and (iii) RoBERTa (Robustly optimized BERT approach) - trained on 10,000 previously published unstructured reports on cannabis-derived products (CDPs) annotated by humans for the presence of adverse events to determine the best-performing AE detection method. This method was then benchmarked against an AI chatbot (ChatGPT: gpt-3.5-turbo-0613) and applied to previously unstudied user narratives about CDPs from 20 subreddits.RoBERTa demonstrated the highest accuracy at 99.7%, hereafter referred to as Waldo, with 22 false positives and 12 false negatives, yielding an F1-score of 95.1% for the positive class. In contrast, the chatbot had an accuracy of 94.4%, with 401 false positives (18.23-fold more than Waldo) and 163 false negatives (13.58-fold more than Waldo), yielding an F1-score of 38% for the positive class. Applying Waldo to 437,132 posts identified 28,832 potential AEs. The subreddit r/Marijuana had the highest AE rate (12.7%), followed by r/weed (10.5%) and r/AskTrees (10.0%). r/weedstocks (0.1%), r/macrogrowery (0.2%), and r/weedbiz (0.2%) had the lowest rates of potential AEs. Waldo addresses critical gaps in safety surveillance for unregulated consumer health products by automatically detecting adverse events from social media-a capability absent in traditional industry systems. Unlike existing approaches limited to structured databases or narrow domains, Waldo processes informal user narratives at scale with high precision. We have open-sourced Waldo for immediate application by the health community [https://waldo-ae-detection.github.io/WALDO/].</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 9","pages":"e0001011"},"PeriodicalIF":7.7,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202372","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}