PLOS digital health最新文献

{"title":"Taking a closer look: Can an app improve diagnostic accuracy in urgent care? Cluster-randomized interventional trial DASI.","authors":"Eva Maria Noack, Kai Antweiler, Tim Friede, Frank Müller, Tobias Schmidt, Eva Hummers, Lea Roddewig, Dominik Schröder","doi":"10.1371/journal.pdig.0001252","DOIUrl":"10.1371/journal.pdig.0001252","url":null,"abstract":"<p><p>In urgent care settings, efficient medical history-taking is paramount for making timely and accurate treatment decisions. Medical history-taking apps have emerged as a means to streamline this process but their effectiveness in enhancing diagnostic accuracy remains unclear. We aimed to investigate whether using a medical history-taking app before consultation improves diagnostic accuracy. In two German out-of-hours practices (OOHP), patients were recruited over a 12-months period. Within each practice, weeks were randomized to either an intervention or control group, resulting in a cluster-randomized trial (CRT) with clustering in weeks within the same practice. Patients in the intervention group used an app to report their complaints before their consultation, enabling physicians to review their medical history details beforehand. In contrast, patients in the control group used the app after their consultation, and no summary of their medical history was available to the physician. Diagnostic accuracy was defined as the agreement between the OOHP physician's diagnoses and those determined by an expert committee (EC) after reviewing patient files. As a secondary outcome, we compared OOHP and EC physicians' treatment recommendations against patients' self-reported actual treatment (e.g., specialist care, hospital admissions) from a follow-up survey. We analyzed data from 986 patients and found no significant intervention effect on diagnostic accuracy (Odds Ratio 0.94 (95%CI 0.73 - 1.21), 57.6% in intervention vs 59.1% in control group). Additionally, the app had no significant effect on the prediction of further treatment. The only significant factors affecting these outcomes were the number of diagnoses (positively associated with diagnostic accuracy) and a self-reported severe condition (associated with higher likelihood of requiring further treatment). Individual differences between physicians were more pronounced than those between the intervention and control group for the secondary outcome. The study's findings suggest that this medical history-taking app does not enhance diagnostic accuracy in urgent care settings.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 2","pages":"e0001252"},"PeriodicalIF":7.7,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931775/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147286551","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":"Design, implementation and analysis of a quality assurance process for Informed Consents using the DZHK registry TORCH-DZHK1 as an example.","authors":"Dana Stahl, Katrin Leyh, Alexander Rudolph, Arne Blumentritt, Kerstin Weitmann, Monika Kraus, Johannes Trebing, Julia Hoffmann, Farbod Sedaghat-Hamedani, Benjamin Meder, Wolfgang Hoffmann","doi":"10.1371/journal.pdig.0000798","DOIUrl":"10.1371/journal.pdig.0000798","url":null,"abstract":"<p><p>To collect sensitive patient data during clinical trials, the Informed Consent (IC) of the participants must be obtained beforehand. If the IC is not correct and complete, the document cannot be used to represent the will of the participant and will not be considered a legally valid document. However, few studies have examined the quality of the IC and the IC-quality found is unfortunately not satisfactory. The aim of this article is to describe the development of an IC quality assurance concept and to report the results of an evaluation using the example of a German Centre for Cardiovascular Research (DZHK) registry. All quality issues identified during the study were documented. These were aggregated into the quality indicators \"Completeness\", \"Consistency of Data\", \"Correctness\" and \"Validity\". Of 2,453 ICs, 1,588 had at least one quality issue; 99.8% of them were resolved. In addition, training sessions were conducted with study staff to raise awareness of the importance of correct IC collection, including documentation, and to minimize quality issues. Our data exemplify that improvements in the recording of ICs by the study staff can be achieved. This evaluation shows the value and importance of continuous IC quality control.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 2","pages":"e0000798"},"PeriodicalIF":7.7,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147286217","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":"ICD coding of death certificates with generative language models.","authors":"Isabel Coutinho, Gonçalo M Correia, Bruno Martins, Afonso Moreira, André Peralta-Santos","doi":"10.1371/journal.pdig.0001245","DOIUrl":"10.1371/journal.pdig.0001245","url":null,"abstract":"<p><p>Although large language models can achieve remarkable results in most text generation tasks, these models have been less used in text classification problems, of which ICD coding of clinical documents is one example. In this work, we propose different strategies to adapt a LLaMA generative language model to the ICD coding task. In one such strategy, we only use a language modeling objective for training, followed by constrained decoding at inference time, rather than fine-tuning the model for discriminative classification. We specifically use free-text descriptions in Portuguese death certificates to train a relatively small LLaMA model for assigning ICD codes to the underlying cause of death, and we compare it against a BERT encoder model, which is typically used to address text classification tasks. Experiments show that generative language models can achieve strong results in ICD coding of death certificates, with a classification accuracy that is at least in line with the results obtained using encoder models. We thus demonstrate that language generation can be a suitable approach for ICD coding, allowing for multiple related tasks, such as coding the underlying or the multiple causes contributing for a death, to be performed with a single unified model.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 2","pages":"e0001245"},"PeriodicalIF":7.7,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147286268","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":"SomaVR: A low-cost virtual reality platform and implementation framework for medical education in resource-limited settings.","authors":"Mike Nsubuga, Grace Kebirungi, Helen Please, Paul Buyego, Henry Mutegeki, Rodgers Kimera, Jag Dhanda, Phil Cruz, Meghan McCarthy, Darrell Hurt, Maria Y Giovanni, Christopher Whalen, Michael Tartakovsky, Daudi Jjingo","doi":"10.1371/journal.pdig.0001253","DOIUrl":"10.1371/journal.pdig.0001253","url":null,"abstract":"<p><p>Quality medical training is vital for effective healthcare worldwide. In low- and middle-income countries (LMICs), traditional training methods often face significant challenges, including limited resources, logistical barriers, and difficulties in safely replicating high-risk scenarios for infectious diseases like COVID-19 and Ebola. Additionally, medical training demands high costs, significant time, and specialized supervision, limiting its accessibility. Although virtual reality (VR) offers promising solutions to these problems, most evidence comes from high-income settings, leaving limited guidance on implementation in resource-constrained settings. We developed SomaVR, a low-cost VR platform and implementation framework for medical training in LMICs. Built with Unity3D, 'SomaVR' (soma - Swahili/Luganda for \"to learn\") integrates 360-degree and interactive virtual environments to create customizable training experiences aligned with specific curricula needs. Beyond the software, the framework provides a structured approach covering hardware selection, software architecture, content development workflows, and strategies for local capacity building. The platform prioritizes cross-platform compatibility, offline functionality, and cost-effective deployment. SomaVR's modular components support both high-end VR systems and low-cost solutions such as smartphone-based. The platform and framework were validated through two independent case studies: 1. COVID-19 infection prevention; and 2. Surgical training. In the surgical training, trainers from a high-income country guided Ugandan learners remotely, illustrating SomaVR's potential for long-distance knowledge exchange. In both cases, cohorts trained using SomaVR consistently outperformed those receiving conventional training, with significant improvements in procedural understanding and user engagement. Our findings also highlight that as VR technology costs decline, frugal approaches such as delivering 360-degree video via smartphone can maintain educational effectiveness in low-resource environments. This paper provides a practical blueprint for developing and implementing sustainable VR medical training platforms in resource-limited settings. By detailing the technical framework, development processes, and implementation strategies of SomaVR, we offer a replicable model for institutions seeking to leverage VR technology for medical education in LMICs.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 2","pages":"e0001253"},"PeriodicalIF":7.7,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12928446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147277816","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":"Trialling the efficacy of a technological visuo-cognitive training program as a compensatory tool for visual rehabilitation after stroke: A pilot study.","authors":"Lewis Jefferson, Abbey Fletcher, Beckie Morris, Julia Das, Rosie Morris, Samuel Stuart, Stephen Dunne","doi":"10.1371/journal.pdig.0000781","DOIUrl":"10.1371/journal.pdig.0000781","url":null,"abstract":"<p><p>Visual impairments are common post-stroke and can lead to diminished functioning and difficulty accomplishing everyday tasks, such as reading and navigating unfamiliar environments independently. This pilot study investigates the usability, acceptability and preliminary efficacy of technological visuo-cognitive training (TVT) using the Senaptec Sensory Station for stroke survivors with visual field loss. Ten stroke survivors (8 males, 2 females; 43-79 years old; Mage = 65, SDage = 11.03) with a non-progressive visual field defect underwent TVT comprising baseline assessment, five 30-minute training sessions over 2-3 weeks, and post-intervention assessment. Measures of visual cognition, patient-reported outcomes, usability, and acceptability were assessed pre- and post-intervention, supplemented by qualitative interviews. Participants demonstrated meaningful gains in several aspects of visual search and functional vision. Reaction times on target capture tasks improved significantly, mirrored by more efficient performance on the Bell's Test. These behavioural changes aligned with reductions in reported visual difficulties and fatigue, both showing large effect sizes. Across sessions, participants also showed improvement in hand-eye coordination and visuomotor integration. Engagement with the system was high: perceived competence increased and usability ratings were excellent. Qualitative accounts contextualised these findings, describing enjoyment of the technology, occasional challenges related to adaptive difficulty or physical limitations, and perceived benefits such as greater awareness of visual scanning strategies in daily life. Notably, several sensory measures (e.g., visual clarity, contrast sensitivity, depth perception) remained unchanged, indicating that improvements were domain-specific rather than global. Overall, TVT demonstrated acceptability with selective improvements in visual search function and vision-related quality of life. Larger randomised controlled trials are needed to determine efficacy and comparative effectiveness against standard rehabilitation approaches.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 2","pages":"e0000781"},"PeriodicalIF":7.7,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12928402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147277852","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 mixed methods evaluation of a pilot open trial of a mentor-guided digital intervention for youth anxiety.","authors":"Emma C Wolfe, Alexandra Werntz, Audrey Michel, Yiyang Zhang, Mark Rucker, Mehdi Boukhechba, Laura E Barnes, Jean E Rhodes, Bethany A Teachman","doi":"10.1371/journal.pdig.0001187","DOIUrl":"10.1371/journal.pdig.0001187","url":null,"abstract":"<p><p>Digital mental health interventions (DMHIs), such as cognitive bias modification for interpretations (CBM-I), offer promise for increasing access to anxiety treatment among underserved adolescents, but data regarding their efficacy are mixed. Paraprofessionals and other caring adults in youth's lives, such as non-parental adult mentors, may be able to support the use of DMHIs and increase teen engagement. The present mixed methods evaluation of a pilot open trial tested the feasibility, acceptability, and preliminary efficacy of implementing MindTrails Teen (an app-based, youth-adapted version of the web-based MindTrails CBM-I intervention) within mentor/mentee dyads. Thirty participants (composed of 15 dyads) participated in remote data collection for 5 weeks. A subset of participants (n = 7 mentors; n = 7 mentees) also provided qualitative feedback. Intervention outcomes (change in anxiety symptoms, and positive and negative interpretation bias), feasibility, and acceptability were assessed via a mix of qualitative interviews, quantitative change in questionnaire scores, and program completion and fidelity metrics. Outcomes were compared to pre-registered benchmarks. Large effect sizes were observed for changes in anxiety among youth. Small to medium effects were observed for change in positive interpretation bias, and no change was found for negative interpretation bias. Intervention outcomes should be considered with caution given very low internal consistency of the interpretation bias measure and the lack of a control comparison group. Acceptability of the intervention was rated positively by mentors and youth. Feasibility benchmarks were met for mentors but not for youth. Qualitative feedback indicated mentors perceived the app as helpful to their mentees, found that it either improved or did not affect their relationship, but also identified implementation challenges. Youth overall perceived the app as helpful but identified barriers to engagement.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 2","pages":"e0001187"},"PeriodicalIF":7.7,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12928454/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147277889","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":"First-line risk stratification with machine learning models facilitates rapid triage for non-ST-elevation myocardial infarction.","authors":"Wei-Jia Luo, Yih-Mei Liou, Cheng-Han Hsiao, Chi-Sheng Hung, Heng-Yu Pan, Chien-Hua Huang, Pan-Chyr Yang, Kang-Yi Su","doi":"10.1371/journal.pdig.0001260","DOIUrl":"10.1371/journal.pdig.0001260","url":null,"abstract":"<p><p>Timely diagnosis of non-ST-elevation myocardial infarction (NSTEMI) remains challenging, as current protocols rely on serial high-sensitivity cardiac troponin (hs-cTn) tests that may delay decisions and overcrowd emergency departments. We retrospectively analyzed 54,636 patients receiving hs-cTn testing at emergency departments across Taiwan (May 2016-Dec 2021). Excluding STEMI and incomplete cases, we developed a machine learning (ML) model using demographics and 23 routine lab tests from the initial blood draw to enable early NSTEMI risk stratification. An actionable clinical decision supporting algorithm was also created based on ML-derived risk scores. A total of 15,096 eligible patients (mean age 69.94 ± 15.66 years; 42.2% female) were included in model training and evaluation. The ML model outperformed hs-cTn alone in both internal and external validation sets in terms of area under the receiver-operating characteristic curve. Beyond model development, a clinically actionable decision algorithm using risk score was established. Thresholds (<1.8 and ≥38.5) to define low- and high-risk groups, the model achieved a negative predictive value (NPV) of 98.8% (98.5-99.1%) for rule-out and a positive predictive value (PPV) of 78.1% (73.2-82.4%) for rule-in, encompassing 48.3% and 2.6% of patients, respectively. When combined with the established 0 h/1 h algorithm, the ML model further enhanced early decision-making, safely ruling in/out 85.3% of patients within 1 hour, with PPV and NPV reaching 84.9% (79.5-87.7%) and 100% (99.6-100%), respectively. In conclusion, this ML-based approach offers not only accurate prediction but also an actionable guide to support rapid, safe NSTEMI triage in emergency care.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 2","pages":"e0001260"},"PeriodicalIF":7.7,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12928466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147277886","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":"Classification of knowledge of fertility period among adolescent girls in East Africa from 2012 to 2022: Machine learning algorithm.","authors":"Andualem Addisu Birlie, Kassahun Dessie Gashu, Mulugeta Desalegn Kasaye, Ayana Alebachew Muluneh, Abdulaziz Kebede Kassaw, Hailemariam Kassahun Desalegn, Tamir Wondim Desta, Shimels Derso Kebede","doi":"10.1371/journal.pdig.0001108","DOIUrl":"10.1371/journal.pdig.0001108","url":null,"abstract":"<p><p>Understanding the time of the menstrual cycle would help women to avoid getting pregnant without the need for surgical, hormonal, or mechanical contraception. Women who do not use contraception and do not know when they are fertile are at a higher risk (17%) of unplanned pregnancy and abortion. Classifying knowledge of fertility periods using machine learning algorithms would help to automate decision-making, produce more precise and accurate classification, and scale up to manage big and complex datasets. Therefore, this study aimed to classify knowledge of the fertility period among adolescent girls in East Africa from 2012 to 2022 using a machine-learning algorithm. A community-based cross-sectional study design was used from 12 East African countries' DHS datasets spanning 2012-2022. The machine learning algorithms were applied to classify knowledge of the fertility period and identify its predictors using R software and Python, particularly Jupiter Notebook in Anaconda. Data cleaning, one-hot encoding, data splitting, data balancing, and ten-fold cross-validation were performed. Ten machine learning algorithms and SHAP were used to select and interpret the best model. From the 40,664 adolescent girls in East Africa, 13.22% (95% CI: 12.91, 13.54) of participants had knowledge of the fertility period. Logistic regression was found to be the best model for unbalanced training data with 74.38% of an AUC and 82.71% of an accuracy. While random forest outperformed on balanced training data, it achieved 91.12% of an AUC and 83.26% accuracy. The key determinant factors of the knowledge of the fertility period were education level, country, hearing about family planning, hearing about sexually transmitted infections, wealth index, knowledge of any method, and visiting health facilities. Governments, NGOs, policy makers, and researchers can utilize these findings to design targeted interventions for improving adolescents' reproductive health based on the identified gaps and disparities.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 2","pages":"e0001108"},"PeriodicalIF":7.7,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12928492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147277839","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":"Topologically distinct 2D and 3D intratumoral heterogeneity scores for preoperatively predicting invasiveness in stage I lung adenocarcinoma: A multicenter study.","authors":"Zhichao Zuo, Xiaohong Fan, Ying Zeng, Wanyin Qi, Wen Liu, Wei Li, Qi Liang","doi":"10.1371/journal.pdig.0001246","DOIUrl":"10.1371/journal.pdig.0001246","url":null,"abstract":"<p><p>This multicenter study aims to enhance the preoperative prediction of pathological invasiveness in clinical stage I lung adenocarcinoma (LUAD) by developing and validating topologically distinct 2D and 3D intratumoral heterogeneity (ITH) scores derived from chest CT imaging. Patients with histopathologically confirmed LUAD were enrolled from three medical centers. We established a dual-scale computational framework to quantify ITH: the 2D ITH score was derived by integrating local radiomics features with global pixel distribution patterns on the largest cross-sectional slice, while the 3D ITH score captured volumetric heterogeneity using a voxel-based topology-aware approach. Subsequently, six machine learning models integrating clinicoradiologic (CR) features with these heterogeneity scores were developed. Model performance was optimized based on the area under the curve (AUC) across a training set and validated in both an internal test set and an independent external validation set. A total of 1,238 eligible patients were enrolled. Centers 1 and 2 provided 1,053 patients (Training: n=737; Internal Test: n=316), while Center 3 provided 185 patients for external validation. The CatBoost classifier integrating 2D/3D ITH scores with CR features (2DITH-3DITH-CR CatBoost) exhibited superior diagnostic performance, achieving AUCs of 0.867 in the internal test set and 0.881 in the external validation set. The integration of topologically distinct 3D ITH scores significantly improves the preoperative stratification of LUAD invasiveness. The 2DITH-3DITH-CR CatBoost model serves as a robust, non-invasive tool to guide individualized surgical decision-making in clinical practice.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 2","pages":"e0001246"},"PeriodicalIF":7.7,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12923145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146260274","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":"Running a clinical trial remotely: Lessons learnt from a decentralised multicentre randomised controlled trial evaluating a digital health intervention for Chronic Kidney Disease.","authors":"Gurneet Kaur Sohansoha, Noemi Vadaszy, Ella C Ford, Thomas J Wilkinson, Matthew Graham-Brown, Alice C Smith, Courtney J Lightfoot","doi":"10.1371/journal.pdig.0001166","DOIUrl":"10.1371/journal.pdig.0001166","url":null,"abstract":"<p><p>Decentralised clinical trials (DCTs) are a potentially efficient and cost-effective way of delivering research trials. My Kidneys & Me, a self-management digital health intervention for chronic kidney disease, was evaluated in a multi-centre randomised DCT (SMILE-K) (ISRCTN18314195). This study aims to evaluate recruitment outcomes and research staff experiences of delivering the SMIKE-K DCT, to inform the design of future DCTs. SMILE-K used fully remote trial processes, including online outcome measure collection. Recruitment and retention data were collected, including numbers invited, recruited, and completing outcome measures, and methods of invitation and consent. Quantitative data were analysed descriptively. Following trial recruitment, semi-structured interviews were conducted with research staff at external recruiting sites to explore their perspectives and experiences of remote trial processes. Qualitative data were analysed using thematic analysis. 420 participants were recruited to SMILE-K. The median time from expression of interest to consent was 1 day (range:0-100), and from consent to randomisation was 6 days (range:0-197). Thirteen research staff were interviewed. Six themes were identified: 'discordance between perceptions and experiences of recruiting participants', 'reallocation of available resources across research studies', 'more environmentally friendly', 'onus on participants', 'engaging disadvantaged groups of participants', and 'future considerations to improve recruitment'. Results suggest that a DCT design can reach a high number of eligible participants. An invitation flyer via post after a remote clinical appointment was the most successful method of recruitment. Research staff felt DCTs provided opportunities for a diverse and representative population to participate and study procedures were environmentally friendly; however, consideration must be given to the factors that may affect recruitment and participation. Our research highlights a clear disparity between the expected recruitment rate and the reality of recruiting for DCTs, with research staff indicating they faced unanticipated challenges during the process. We outline factors for consideration when designing and delivering DCTs.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 2","pages":"e0001166"},"PeriodicalIF":7.7,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12923010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146260241","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}