PLOS digital health最新文献

{"title":"Sociodemographically differential patterns of chronic pain progression revealed by analyzing the all of us research program data.","authors":"Edwin Baldwin, Jin Zhou, Wenting Luo, W Michael Hooten, Jungwei W Fan, Haiquan Li","doi":"10.1371/journal.pdig.0000687","DOIUrl":"10.1371/journal.pdig.0000687","url":null,"abstract":"<p><p>The differential progression of ten chronic overlapping pain conditions (COPC) and four comorbid mental disorders across demographic groups have rarely been reported in the literature. To fill in this gap, we conducted retrospective cohort analyses using All of Us Research Program data from 1970 to 2023. Separate cohorts were created to assess the differential patterns across sex, race, and ethnicity. Logistic regression models, controlling for demographic variables and household income level, were employed to identify significant sociodemographic factors associated with the differential progression from one COPC or mental condition to another. Among the 139 frequent disease pairs, we identified group-specific patterns in 15 progression pathways. Black or African Americans with a COPC condition had a significantly increased association in progression to other COPCs (CLBP- > IBS, CLBP- > MHA, or IBS- > MHA, OR≥1.25, adj.p ≤ 4.0x10-3) or mental disorders (CLBP- > anxiety, CLBP- > depression, MHA- > anxiety, MHA- > depression, OR≥1.25, adj.p ≤ 1.9x10-2) after developing a COPC. Females had an increased likelihood of chronic low back pain after anxiety and depression (OR≥1.12, adj.p ≤ 1.5x10-2). Additionally, the lowest income bracket was associated with an increased risk of developing another COPC from a COPC (CLBP- > MHA, IBS- > MHA, MHA- > CLBP, or MHA- > IBS, OR≥1.44, adj.p ≤ 2.6x10-2) or from a mental disorder (depression- > MHA, depression- > CLBP, anxiety- > CLBP, or anxiety- > IBS, OR≥1.50, adj.p ≤ 2.0x10-2), as well as developing a mental disorder after a COPC (CLBP- > depression, CBLP- > anxiety, MHA- > anxiety, OR≥1.37,adj.p ≤ 1.6x10-2). To our knowledge, this is the first study that unveils the sociodemographic influence on COPC progression. These findings suggest the importance of considering sociodemographic factors to achieve optimal prognostication and preemptive management of COPCs.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000687"},"PeriodicalIF":0.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12173358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318901","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":"Employing transfer learning for breast cancer detection using deep learning models.","authors":"Frimpong Twum, Charlyne Carol Eyram Ahiable, Stephen Opoku Oppong, Linda Banning, Kwabena Owusu-Agyemang","doi":"10.1371/journal.pdig.0000907","DOIUrl":"10.1371/journal.pdig.0000907","url":null,"abstract":"<p><p>Breast cancer remains a critical global health concern, affecting countless lives worldwide. Early and accurate detection plays a vital role in improving patient outcomes. The challenge lies with the limitations of traditional diagnostic methods in terms of accuracy. This study proposes a novel model based on the four pretrained deep learning models, Mobilenetv2, Inceptionv3, ResNet50, and VGG16, which were also used as feature extractors and fed on multiple supervised learning models using the BUSI dataset. Mobiletnetv2, inceptionv3, ResNet50 and VGG16 achieved an accuracy of 85.6%, 90.8%, 89.7% and 88.06%, respectively, with Logistic Regression and Light Gradient Boosting Machine being the best performing classifiers. Using transfer learning, the top layers of the model were frozen, and additional layers were added. A GlobalAveragePooling2D layer was employed to reduce spatial dimensions of the input image. After training and testing based on the accuracy, ResNet50 performed the best with 95.5%, followed by Inceptionv3 92.5%, VGG16 86.5% and lastly Mobilenetv2 84%.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000907"},"PeriodicalIF":0.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12169539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310881","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":"Analysing health misinformation with advanced centrality metrics in online social networks.","authors":"Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao","doi":"10.1371/journal.pdig.0000888","DOIUrl":"10.1371/journal.pdig.0000888","url":null,"abstract":"<p><p>The rapid spread of health misinformation on online social networks (OSNs) during global crises such as the COVID-19 pandemic poses challenges to public health, social stability, and institutional trust. Centrality metrics have long been pivotal in understanding the dynamics of information flow, particularly in the context of health misinformation. However, the increasing complexity and dynamism of online networks, especially during crises, highlight the limitations of these traditional approaches. This study introduces and compares three novel centrality metrics: dynamic influence centrality (DIC), health misinformation vulnerability centrality (MVC), and propagation centrality (PC). These metrics incorporate temporal dynamics, susceptibility, and multilayered network interactions. Using the FibVID dataset, we compared traditional and novel metrics to identify influential nodes, propagation pathways, and misinformation influencers. Traditional metrics identified 29 influential nodes, while the new metrics uncovered 24 unique nodes, resulting in 42 combined nodes, an increase of 44.83%. Baseline interventions reduced health misinformation by 50%, while incorporating the new metrics increased this to 62.5%, an improvement of 25%. To evaluate the broader applicability of the proposed metrics, we validated our framework on a second dataset, Monant Medical Misinformation, which covers a diverse range of health misinformation discussions beyond COVID-19. The results confirmed that the advanced metrics generalised successfully, identifying distinct influential actors not captured by traditional methods. In general, the findings suggest that a combination of traditional and novel centrality measures offers a more robust and generalisable framework for understanding and mitigating the spread of health misinformation in different online network contexts.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000888"},"PeriodicalIF":0.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12169528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310880","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":"Missed opportunities for digital health data use in healthcare decision-making: A cross-sectional digital health landscape assessment in Homa Bay county, Kenya.","authors":"Mercy Chepkirui, Stephanie Dellicour, Rosemary Musuva, Isdorah Odero, Benson Omondi, Benard Omondi, Eric Onyango, Hellen Barsosio, Lilian Otiso, Gordon Okomo, Maina Waweru, Maia Lesosky, Tara Tancred, Yussif Alhassan, Simon Kariuki, Feiko terKuile, Miriam Taegtmeyer","doi":"10.1371/journal.pdig.0000870","DOIUrl":"10.1371/journal.pdig.0000870","url":null,"abstract":"<p><p>The proliferation of digital health systems in Sub-Saharan Africa is driven by the need to improve healthcare access and decision-making. This digitisation has been marked by fragmented implementation, the absence of universal patient identifiers, inadequate system linkages, limited data sharing, and reliance on donor-driven funding. Consequently, the increase in digital health data generation is not matched by similar growth in data use for decision-making, patient-centric care, and research. This study aimed to describe the digital health landscape in Homa Bay County and highlight the strengths and limitations of using digital health data for healthcare decision-making. We used mixed methods. A cross-sectional survey was conducted between June 2022 and October 2023 in 112 healthcare facilities to identify available digital health systems and assess their adoption and utilisation. Thirty-three in-depth interviews were conducted with relevant digital health stakeholders to seek stakeholder perspectives. Our study identified ten different digital health systems, nine of which were in active use. 91% (102/112) of surveyed health facilities had Kenya Electronic Medical Record system deployed for HIV patient management. Eight additional digital systems were available alongside this HIV system, but deployment was fragmented. Challenges to digital systems usage included lack of interoperability, unreliable internet, system downtime, power outages, staff turnover, patient workload, and lack of universal patient identifiers. The study identified multiple systems in use, with the HIV care management system being the most prevalent. The primary challenge hindering effective digital data utilisation is network instability, alongside issues such as the lack of interoperability, disjointed data quality assurance processes, and non-standardised patient identifiers. Recommendations include establishing a routine care data governance framework, implementing universal unique patient identifiers, harmonised data quality practices, advocating for universally compatible digital systems, promoting interoperability, and evaluating the suitability of the existing digital health data for surveillance research and decision-making.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000870"},"PeriodicalIF":0.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12165431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289711","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":"Logged in, not lagging behind: A purpose-oriented use of technology for youth health.","authors":"Matea Cañizares","doi":"10.1371/journal.pdig.0000881","DOIUrl":"10.1371/journal.pdig.0000881","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000881"},"PeriodicalIF":0.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12165341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289769","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":"Implementation costs and cost-effectiveness of ultraportable chest X-ray with artificial intelligence in active case finding for tuberculosis in Nigeria.","authors":"Tushar Garg, Stephen John, Suraj Abdulkarim, Adamu D Ahmed, Beatrice Kirubi, Md Toufiq Rahman, Emperor Ubochioma, Jacob Creswell","doi":"10.1371/journal.pdig.0000894","DOIUrl":"10.1371/journal.pdig.0000894","url":null,"abstract":"<p><p>Availability of ultraportable chest x-ray (CXR) and advancements in artificial intelligence (AI)-enabled CXR interpretation are promising developments in tuberculosis (TB) active case finding (ACF) but costing and cost-effectiveness analyses are limited. We provide implementation cost and cost-effectiveness estimates of different screening algorithms using symptoms, CXR and AI in Nigeria. People 15 years and older were screened for TB symptoms and offered a CXR with AI-enabled interpretation using qXR v3 (Qure.ai) at lung health camps. Sputum samples were tested on Xpert MTB/RIF for individuals reporting symptoms or with qXR abnormality scores ≥0.30. We conducted a retrospective costing using a combination of top-down and bottom-up approaches while utilizing itemized expense data from a health system perspective. We estimated costs in five screening scenarios: abnormality score ≥0.30 and ≥0.50; cough ≥ 2 weeks; any symptom; abnormality score ≥0.30 or any symptom. We calculated total implementation costs, cost per bacteriologically-confirmed case detected, and assessed cost-effectiveness using incremental cost-effectiveness ratio (ICER) as additional cost per additional case. Overall, 3205 people with presumptive TB were identified, 1021 were tested, and 85 people with bacteriologically-confirmed TB were detected. Abnormality ≥ 0.30 or any symptom (US$65704) had the highest costs while cough ≥ 2 weeks was the lowest (US$40740). The cost per case was US$1198 for cough ≥ 2 weeks, and lowest for any symptom (US$635). Compared to baseline strategy of cough ≥ 2 weeks, the ICER for any symptom was US$191 per additional case detected and US$ 2096 for Abnormality ≥0.30 OR any symptom algorithm. Using CXR and AI had lower cost per case detected than any symptom screening criteria when asymptomatic TB was higher than 30% of all bacteriologically-confirmed TB detected. Compared to traditional symptom screening, using CXR and AI in combination with symptoms detects more cases at lower cost per case detected and is cost-effective. TB programs should explore adoption of CXR and AI for screening in ACF.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000894"},"PeriodicalIF":0.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12157241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276901","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 impact of mind-body internet and mobile-based interventions on fatigue in adults living with chronic physical conditions: A systematic review and meta-analysis of randomized controlled trials.","authors":"Serena Isley, Emily Johnson, Shaina Corrick, Ashley Hyde, Ben Vandermeer, Naomi Dolgoy, Nathanael Tabert, Edith Pituskin, Puneeta Tandon","doi":"10.1371/journal.pdig.0000878","DOIUrl":"10.1371/journal.pdig.0000878","url":null,"abstract":"<p><p>Chronic physical conditions (CPCs) are conditions that persist for long periods and may not have a cure. Fatigue is a common symptom experienced by people living with CPCs. Mind-body internet and mobile-based interventions (IMIs) offer an accessible management strategy. The objective of this review was to assess the impact of mind-body IMIs on fatigue symptoms in adults with CPCs. Six databases were searched from inception to July 2024. Inclusion required randomized controlled trials (RCTs) of mind-body IMIs in adults (≥ 18) with CPCs that assessed fatigue pre-and post-intervention using self-report questionnaires. The primary outcome was the standardized mean fatigue change scores (Hedges' g). Sub-group analyses were conducted on CPC type, mind-body technique, fatigue questionnaire, and personnel support level. Meta-regression was performed on IMI length and age. Study quality was assessed using the Cochrane Risk of Bias 2.0 tool. The search retrieved 5239 studies. Seventeen studies met inclusion criteria: 47% neurological (n = 8), 29% cancer (n = 5), and 24% autoimmune (n = 4). Seven studies (41%) included cognitive behavioural therapy (CBT), seven used CBT combined with non-CBT techniques, and three employed non-CBT techniques. Mind-body IMIs led to significant reductions in fatigue (SMD = -0.74 [-1.09, -0.39]; p < 0.0001), with a greater effect in younger participants (p = 0.005). Heterogeneity was moderate to high. In conclusion, mind-body IMIs show promise in reducing fatigue symptoms in adults with CPCs. Further high-quality RCTs, expanding beyond CBT techniques, and using at least one common fatigue scale across conditions, would be helpful in evaluating the impact of IMIs across a broader range of CPCs.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000878"},"PeriodicalIF":0.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12157242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276944","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":"Retrieval augmented generation for large language models in healthcare: A systematic review.","authors":"Lameck Mbangula Amugongo, Pietro Mascheroni, Steven Brooks, Stefan Doering, Jan Seidel","doi":"10.1371/journal.pdig.0000877","DOIUrl":"10.1371/journal.pdig.0000877","url":null,"abstract":"<p><p>Large Language Models (LLMs) have demonstrated promising capabilities to solve complex tasks in critical sectors such as healthcare. However, LLMs are limited by their training data which is often outdated, the tendency to generate inaccurate (\"hallucinated\") content and a lack of transparency in the content they generate. To address these limitations, retrieval augmented generation (RAG) grounds the responses of LLMs by exposing them to external knowledge sources. However, in the healthcare domain there is currently a lack of systematic understanding of which datasets, RAG methodologies and evaluation frameworks are available. This review aims to bridge this gap by assessing RAG-based approaches employed by LLMs in healthcare, focusing on the different steps of retrieval, augmentation and generation. Additionally, we identify the limitations, strengths and gaps in the existing literature. Our synthesis shows that 78.9% of studies used English datasets and 21.1% of the datasets are in Chinese. We find that a range of techniques are employed RAG-based LLMs in healthcare, including Naive RAG, Advanced RAG, and Modular RAG. Surprisingly, proprietary models such as GPT-3.5/4 are the most used for RAG applications in healthcare. We find that there is a lack of standardised evaluation frameworks for RAG-based applications. In addition, the majority of the studies do not assess or address ethical considerations related to RAG in healthcare. It is important to account for ethical challenges that are inherent when AI systems are implemented in the clinical setting. Lastly, we highlight the need for further research and development to ensure responsible and effective adoption of RAG in the medical domain.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000877"},"PeriodicalIF":0.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12157099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276902","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":"Exploring the sustainability of virtual care interventions: A scoping review.","authors":"Tujuanna Austin, Farah Tahsin, Darren Larsen, Ross Baker, Carolyn Steele Gray","doi":"10.1371/journal.pdig.0000893","DOIUrl":"10.1371/journal.pdig.0000893","url":null,"abstract":"<p><p>During the COVID-19 pandemic, virtual care has proven instrumental in ensuring the continuity of healthcare services. In the context of virtual care's growing prominence and continued use, understanding how and why virtual care interventions are sustained will help healthcare systems to better prepare for future crises. The objectives of this scoping review were to construct a conceptualization of of virtual care sustainability and to describe factors influencing the sustainability of virtual care, shedding light on the determinants that shape its longevity and continued use. Literature describing the sustainability of virtual care interventions was summarized. Details of the intervention, setting, methodology, description and evidence of sustainability, and synopsis of key findings were documented. The charted data were summarized to gain a descriptive understanding of the data collected and to establish patterns. A conceptualization of virtual care intervention sustainability focused on the concepts of fidelity and adaptability. Sustainability of virtual care interventions were conceptualized as the intervention's ability to continue to be used according to its initial design, the extent to which the intervention continued to achieve its intended outcomes (fidelity), and the ability of the intervention to evolve as the context in which it is used also evolves (adaptability). While there were various definitions of sustainability referenced, no included studies mentioned a definition of sustainability specific to virtual care. Commonalities in definitions included the continued use of virtual care and the continuation of the benefits of virtual care for some period of time. Findings indicate that there is no \"one size fits all\" approach to achieving sustainability of virtual care interventions, but instead identify factors that may support or hinder sustainability. Important to understanding sustainability of virtual care interventions, is the complexity of the interactions that influence it. Specifically, the factors of fidelity and adaptability are found to be important to understanding the sustainability of virtual care interventions.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000893"},"PeriodicalIF":0.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12157087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276900","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 computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach.","authors":"Navreet Kaur, Manuel Gonzales Iv, Cristian Garcia Alcaraz, Jiaqi Gong, Kristen J Wells, Laura E Barnes","doi":"10.1371/journal.pdig.0000839","DOIUrl":"10.1371/journal.pdig.0000839","url":null,"abstract":"<p><p>Non-adherence to medications is a critical concern since nearly half of patients with chronic illnesses do not follow their prescribed medication regimens, leading to increased mortality, costs, and preventable human distress. Amongst stage 0-3 breast cancer survivors, adherence to long-term adjuvant endocrine therapy (i.e., Tamoxifen and aromatase inhibitors) is associated with a significant increase in recurrence-free survival. This work aims to develop multi-scale models of medication adherence to understand the significance of different factors influencing adherence across varying time frames. We introduce a computational framework guided by Social Cognitive Theory for multi-scale (daily and weekly) modeling of longitudinal medication adherence. Our models employ both dynamic medication-taking patterns in the recent past (dynamic factors) as well as less frequently changing factors (static factors) for adherence prediction. Additionally, we assess the significance of various factors in influencing adherence behavior across different time scales. Our models outperform traditional machine learning counterparts in both daily and weekly tasks in terms of both accuracy and specificity. Daily models achieved an accuracy of 87.25% (Precision - 92.04%, Recall - 93.15%, Specificity - 77.50%), and weekly models, an accuracy of 76.04% (Precision - 75.83%, Recall - 85.80%, Specificity - 72.30%). Notably, dynamic past medication-taking patterns prove most valuable for predicting daily adherence, while a combination of dynamic and static factors is significant for macro-level weekly adherence patterns. While our models exhibit strong predictive performance, they are constrained by potential cohort-specific biases, reliance on self-reported adherence data, and a limited understanding of the context around non-adherence. Future research will focus on external validation across diverse populations and explore the real-world implementation of sensor-rich systems for a more comprehensive assessment of medication adherence. Nonetheless, we assessed a theory-informed, multi-scale approach to predict adherence, and our findings offer valuable insights to guide the designing of personalized, technology-driven adherence interventions and fostering collaboration among patients, healthcare providers, and caregivers to support long-term adherence.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000839"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12151371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268057","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}