Mayo Clinic Proceedings. Digital health最新文献

{"title":"Predicting Tolerance to Anthracycline Chemotherapy Using Electrocardiogram-Based Artificial Intelligence in Sarcoma","authors":"Jack B. Korleski MD ,&nbsp;Regina M. Koch MD ,&nbsp;Thanh P. Ho MD ,&nbsp;Steven I. Robinson MBBS ,&nbsp;Scott H. Okuno MD ,&nbsp;Joerg Herrmann MD ,&nbsp;Brittany L. Siontis MD","doi":"10.1016/j.mcpdig.2025.100247","DOIUrl":"10.1016/j.mcpdig.2025.100247","url":null,"abstract":"<div><h3>Objective</h3><div>The objective of this study was to understand the utility of artificial intelligence-enabled electrocardiogram (AI-ECG) to assess the tolerability of anthracycline chemotherapy.</div></div><div><h3>Patients and Methods</h3><div>From December 18, 2006 to October 15, 2020, we identified adults with sarcoma who were treated with anthracycline chemotherapy at our institution who had an ECG within 1 year prior to treatment initiation. Utilizing previously defined AI-ECG nomograms, we obtained age and ejection fraction (EF) predictions. Changes in AI-ECG age were correlated with chemotherapy tolerance (the rates of dose reductions, treatment delays, and early discontinuation). We measured the sensitivity and specificity of the ECG to predict an EF of less than 50% or 35% prior to treatment and compared how changes in the AI-ECG EF prediction related to changes in echocardiography-based EF.</div></div><div><h3>Results</h3><div>Forty patients met the eligibility criteria. Sixty-eight percent of the patients were men. The median age was 56.5 years (18-76 years). We did not find differences in chemotherapy tolerance between patients who had an elevated or decreased ECG age. There was a trend `toward higher rates of dose reductions in patients with high ECG aging (odds ratio, 5.13; <em>P</em>=.32). The AI-ECG low EF prediction had a sensitivity of 100% and a specificity of 94% to isolate patients with an EF of less than 50% prior to treatment. Two patients’ EF decreased more than 10% after treatment, and both cases showed significant increases in the low EF prediction.</div></div><div><h3>Conclusion</h3><div>Overall, AI-based predictions on ECG tracings could be a way to monitor for decreases in EF during treatment with anthracycline chemotherapy. We recommend further studies to evaluate AI-ECG aging as a marker for chemotherapy tolerance.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100247"},"PeriodicalIF":0.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence and Multi-Omics in Pharmacogenomics: A New Era of Precision Medicine","authors":"Mike Zack MD, PhD, MPH,&nbsp;Danil N. Stupichev MSc,&nbsp;Alex J. Moore BSc,&nbsp;Ioan D. Slobodchikov MSc,&nbsp;David G. Sokolov MSc,&nbsp;Igor F. Trifonov MSc,&nbsp;Allan Gobbs MSc","doi":"10.1016/j.mcpdig.2025.100246","DOIUrl":"10.1016/j.mcpdig.2025.100246","url":null,"abstract":"<div><div>Pharmacogenomics is entering a transformative phase as high-throughput “omics” techniques become increasingly integrated with state-of-the-art artificial intelligence (AI) methods. Although early successes in single-gene pharmacogenetics reported clear clinical benefits, many drug response phenotypes are governed by intricate networks of genomic variants, epigenetic modifications, and metabolic pathways. Multi-omics approaches address this complexity by capturing genomic, transcriptomic, proteomic, and metabolomic data layers, offering a comprehensive view of patient-specific biology. Advanced AI models, including deep neural networks, graph neural networks, and representation learning techniques, further enhance this landscape by detecting hidden patterns, filling gaps in incomplete data sets, and enabling in silico simulations of treatment responses. Such capabilities not only improve predictive accuracy but also deepen mechanistic insights, revealing how gene–gene and gene–environment interactions shape therapeutic outcomes. At the same time, real-world data from diverse patient populations is broadening the evidence base, underscoring the importance of inclusive datasets and population-specific algorithms to reduce health disparities. Despite challenges related to data harmonization, interpretability, and regulatory oversight, the synergy between multi-omics integration and AI-driven analytics holds relevant promise for revolutionizing clinical decision-making. In this review, we highlighted key technological advances, discussed current limitations, and outlined future directions for translating multi-omics plus AI innovations into routine personalized medicine.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100246"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Image-Diagnosis for Female Genital Schistosomiasis","authors":"Jiayuan Zhu MSc ,&nbsp;J. Alison Noble DPhil ,&nbsp;Mireille Gomes DPhil","doi":"10.1016/j.mcpdig.2025.100245","DOIUrl":"10.1016/j.mcpdig.2025.100245","url":null,"abstract":"<div><h3>Objective</h3><div>To introduce a novel, artificial Intelligence (AI), deep learning-based application for automated diagnosis of female genital schistosomiasis (FGS), a disease estimated to affect around 56 million women and girls in sub-Saharan Africa.</div></div><div><h3>Patients and Methods</h3><div>This study focused on cervical images collected from a high endemic FGS area in Cameroon, from August 1, 2020 to August 31, 2021. We applied the You Only Look Once deep learning model and employed a 5-fold cross-validation approach, accompanied by sensitivity analysis, to optimize model performance.</div></div><div><h3>Results</h3><div>The model achieved a sensitivity of 0.96 (76/78) and an accuracy of 0.78 (97/125), demonstrating improved performance over an existing, non-AI-based, computerized image diagnostic method, which has a sensitivity of 0.94 (73/78) but an accuracy of 0.58 (73/125) on the same dataset. In addition, the AI model significantly reduced processing time, decreasing from 47 minutes to under 90 seconds for testing 250 images.</div></div><div><h3>Conclusion</h3><div>This study highlights the potential of deep learning-based models for automated diagnosis for FGS while reducing the reliance on specialized clinical expertise. It also underscores the need for further work to address current limitations of such AI-based methods for FGS diagnosis.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100245"},"PeriodicalIF":0.0,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144605925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Healthy Heart Assistant, a WhatsApp-Based Generative Pretrained Transformer Technology, for Self-Care in Hypertensive Patients","authors":"Samuel E. Antia MD, MSc ,&nbsp;Collins N. Ugwu MD, MSc ,&nbsp;Vishal Ghodka BE ,&nbsp;Babangida S. Chori MSc ,&nbsp;Muhammad S. Nazir MD, MSc ,&nbsp;Chizoba A. Odili PhD ,&nbsp;Godsent C. Isiguzo MD, PhD ,&nbsp;Sri Vasireddy MS, MBA ,&nbsp;Augustine N. Odili MD, PhD","doi":"10.1016/j.mcpdig.2025.100243","DOIUrl":"10.1016/j.mcpdig.2025.100243","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the feasibility, usability, and efficacy of innovative generative pretrained transformer chatbot in improving self-care in hypertensive patients in a resource-limited setting.</div></div><div><h3>Patients and Methods</h3><div>A single-arm nonblinded clinical trial was deployed in a busy cardiology clinic in a low-resource setting. Artificial intelligence–enabled chatbot (Healthy Heart Assistant) was activated in smartphones of 50 adults on treatment for hypertension. Participants were trained on how to use the Healthy Heart Assistant including setting medication and appointment reminders. Baseline questionnaires were administered at enrollment and 30 days later to explore acceptability, feasibility and usability of the bot. We used chatbot usability questionnaire and self-made Healthy Heart Assistant satisfaction questionnaire to assess bot usability and patients’ satisfaction, respectively. The study began on April 5, 2024, through July 15, 2024.</div></div><div><h3>Results</h3><div>Of 200 hypertensive clinic attendees, 70 (35%) had internet-enabled bot-compatible cell phones, of which 50 hypertensive patients were recruited to participate in the study. Among 50 participants, 2 (4%) were lost to follow-up; 19 (39.6%) were women; and 40 (83.3%) had attained tertiary level of education. Mean time of training to use bot was 5.7 minutes, with 35 (70.8%) of participants being able to use the bot within 5 minutes. The median frequency of chats for participants within the timeframe was an average of 1.5 chats/day. Chatbot usability questionnaire score was 69.5%, whereas self-made Healthy Heart Assistant satisfaction questionnaire score was 90%.</div></div><div><h3>Conclusion</h3><div>This proof-of-concept study shows that generative artificial intelligence can be applied with reasonable success in hypertension self-care in low-resource settings and has potential for being effective.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100243"},"PeriodicalIF":0.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Digital Self-Diagnosis Tools: A Narrative Overview of Reviews","authors":"Aikaterini Mentzou PhD ,&nbsp;Amy Rogers MD ,&nbsp;Edzia Carvalho PhD ,&nbsp;Angela Daly PhD ,&nbsp;Maeve Malone HDip ,&nbsp;Xaroula Kerasidou PhD","doi":"10.1016/j.mcpdig.2025.100242","DOIUrl":"10.1016/j.mcpdig.2025.100242","url":null,"abstract":"<div><div>Digital self-diagnosis tools, or symptom checkers, many of which incorporate artificial intelligence technology, are intended to provide diagnostic information and triage advice to lay users. This narrative overview of reviews explores the common themes and issues raised by existing evidence synthesis literature on these tools to establish a common ground for interdisciplinary research. We searched 3 bibliographic databases (PubMed, Scopus and Web of Science) and Google Scholar using keyword combinations of <em>artificial</em>, <em>self-diagnosis</em>, <em>intelligence</em>, and <em>machine learning</em> for publications from 2019 to 2023. We included systematic reviews, meta-analyses, scoping reviews, narrative syntheses, and opinion pieces that discussed tools where users proactively entered personal health information to acquire a predicted diagnosis of their symptoms or triage advice. This overview reveals significant gaps in understanding the key areas of development, implementation, impact, and oversight of digital self-diagnosis tools. Additionally, the terminology used to describe these tools and their underlying technologies varies widely, encompassing technologies ranging from simple branching logic algorithms to complex deep neural networks. Our interdisciplinary analysis identified gaps and critical areas for future research across all stages of the lifecycles of these tools. The diverse challenges uncovered highlight the necessity for multiagency and multidisciplinary efforts promoting responsible development and implementation.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100242"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural Language Processing for Enhanced Clinical Decision Support in Allergy Verification for Medication Prescriptions","authors":"Juan Pablo Botero-Aguirre MS ,&nbsp;Michael Andrés García-Rivera MS","doi":"10.1016/j.mcpdig.2025.100244","DOIUrl":"10.1016/j.mcpdig.2025.100244","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and validate a named entity recognition (NER) model based on BERT-based model trained on Spanish-language corpor, for extracting allergy-related information from unstructured electronic health records.</div></div><div><h3>Patients and Methods</h3><div>The model was fine-tuned using 16,176 manually annotated allergy-related entities from anonimized patient records (hospitalized patients between January 1, 2021, and June 30, 2024). The data set was divided into training (80%) and testing (20%) subsets, and model performance was evaluated using accuracy, recall, and F1 score. The validated model was applied to another data set with 80,917 medication prescriptions from 5859 hospitalized patients with at least one prescribed medication (during August and September 2024) to detect potential prescription errors. Sensitivity, specificity, and Cohen κ were calculated using manual expert review as the gold standard.</div></div><div><h3>Results</h3><div>The model achieved an accuracy of 87.28% and an F1 score of 0.80. It effectively identified medication names (F1=0.91) and adverse reactions (F1=0.85) but struggled with recommendation-related entities (F1=0.29). The model detected prescription errors in 0.96% of cases, with a sensitivity of 75.73% and specificity of 99.98%. The weighted κ score (0.7797) indicated substantial agreement with expert annotations.</div></div><div><h3>Conclusion</h3><div>The BERT-based model trained on Spanish-language corpora–based NER model demonstrated strong performance in identifying nonallergic cases (specificity, 99.98%; negative predictive value, 99.97%) and showed promise for clinical decision support. Despite moderate sensitivity (75.73%), these results highlight the feasibility of using Spanish-language NER models to enhance medication safety.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100244"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medication Adherence Technologies: A Classification Taxonomy Based on Features","authors":"Bincy Baby PharmD, MSc ,&nbsp;Jasdeep Kaur Gill PharmD ,&nbsp;Sadaf Faisal BPharm, PhD ,&nbsp;Ghada Elba PharmD, MSc ,&nbsp;SooMin Park PharmD (c) ,&nbsp;Annette McKinnon ,&nbsp;Kirk Patterson BA ,&nbsp;Sara J.T. Guilcher PT, PhD ,&nbsp;Feng Chang PharmD ,&nbsp;Linda Lee MD ,&nbsp;Catherine Burns PhD ,&nbsp;Ryan Griffin PhD ,&nbsp;Tejal Patel BScPharm, PharmD","doi":"10.1016/j.mcpdig.2025.100237","DOIUrl":"10.1016/j.mcpdig.2025.100237","url":null,"abstract":"<div><h3>Objective</h3><div>To develop a comprehensive classification system for medication adherence technologies based on an inventory of characteristics and features of existing technology.</div></div><div><h3>Participants and Methods</h3><div>Using a 3-stage approach methodology—development, validation, and evaluation, the study adopted the taxonomy development method and was conducted from February 1, 2023 to July 31, 2024. In the development stage, medication adherence technologies were defined, end users were identified, and a meta-characteristic was determined; using both empirical-to-conceptual and conceptual-to-empirical approaches, dimensions and characteristics were identified. The taxonomy was validated through the Delphi consensus approach and classifying 20 sample medication adherence technologies and evaluated by mapping to codes identified from a qualitative study.</div></div><div><h3>Results</h3><div>After undergoing 8 iterations, which included incorporating feedback from a Delphi consensus survey, the final taxonomy comprised 7 dimensions, 25 subdimensions, and 320 characteristics. These key dimensions include Physical Features, Display, Connectivity, System Alert, Data Collection and Management, Operations, and Integration. The taxonomy was considered complete and valuable once all preestablished ending conditions were met, and its applicability and comprehensiveness were verified by comparing various medication adherence technologies and mapping to codes identified from a qualitative study.</div></div><div><h3>Conclusion</h3><div>This study successfully establishes the first comprehensive classification system for medication adherence technologies based on features, addressing a critical gap in literature. The taxonomy provides a structured framework for categorizing and evaluating technologies, supporting usability testing and the selection of appropriate devices tailored to the unique needs of older adults.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100237"},"PeriodicalIF":0.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Evaluation of an Artificial Intelligence–Powered Surgical Oral Examination Simulator: A Pilot Study","authors":"Arya S. Rao BA ,&nbsp;Siona Prasad BA ,&nbsp;Richard S. Lee BS ,&nbsp;Susan Farrell MD ,&nbsp;Sophia McKinley MD, MED ,&nbsp;Marc D. Succi MD","doi":"10.1016/j.mcpdig.2025.100241","DOIUrl":"10.1016/j.mcpdig.2025.100241","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and validate an artificial intelligence–powered platform that simulates surgical oral examinations, addressing the limitations of traditional faculty-led sessions.</div></div><div><h3>Patients and Methods</h3><div>This cross-sectional study, conducted from June 1, 2024, through December 1, 2024, comprised technical validation and educational assessment of a novel large language model (LLM)–based surgical education tool (surgery oral examination large language model [SOE-LLM]). The study involved 12 surgical clerkship students completing their core rotation at a major academic medical center. The SOE-LLM, using MIMIC-IV–derived surgical cases (acute appendicitis and pancreatitis), was implemented to simulate oral examinations. Technical validation assessed performance across 8 domains: case presentation accuracy, physical examination findings, historical detail preservation, laboratory data reporting, imaging interpretation, management decisions, and recognition of contraindicated interventions. Educational utility was evaluated using a 5-point Likert scale.</div></div><div><h3>Results</h3><div>Technical validation showed the SOE-LLM’s ability to function as a consistent oral examiner. The model accurately guided students through case presentations, responded to diagnostic questions, and provided clinically sound responses based on MIMIC-IV cases. When tested with standardized prompts, it maintained examination fidelity, requiring proper diagnostic reasoning and differentiating operative versus medical management. Student evaluations highlighted the platform’s value as an examination preparation tool (mean, 4.250; SEM, 0.1794) and its ability to create a low-stakes environment for high-stakes decision practice (mean, 4.833; SEM, 0.1124).</div></div><div><h3>Conclusion</h3><div>The SOE-LLM shows potential as a valuable tool for surgical education, offering a consistent and accessible platform for simulating oral examinations.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100241"},"PeriodicalIF":0.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept Study","authors":"Stephanie Zawada PhD, MS ,&nbsp;Jestrii Acosta MS ,&nbsp;Caden Collins BA ,&nbsp;Oana Dumitrascu MD, MS ,&nbsp;Ehab Harahsheh MBBS ,&nbsp;Clinton Hagen MS ,&nbsp;Ali Ganjizadeh MD ,&nbsp;Elham Mahmoudi MD ,&nbsp;Bradley Erickson MD, PhD ,&nbsp;Bart Demaerschalk MD, MSc","doi":"10.1016/j.mcpdig.2025.100240","DOIUrl":"10.1016/j.mcpdig.2025.100240","url":null,"abstract":"<div><h3>Objective</h3><div>To assess the feasibility of using smartphones to longitudinally collect objective behavior measures and establish the extent to which they can predict gold-standard depression severity in patients with ischemic stroke and transient ischemic attack (IS/TIA) symptoms.</div></div><div><h3>Patients and Methods</h3><div>Participants with IS/TIA symptoms were monitored in real-world settings using the Beiwe application for 8 or more weeks during March 1, 2024 to November 15, 2024. Depression symptoms were tracked via weekly Patient Health Questionnaire (PHQ)-8 surveys, monthly personnel-administered Montgomery–Åsberg Depression Rating Scale (MADRS) assessments, and weekly averages of smartphone sensor measures. Repeated measures correlation established associations between PHQ-8 scores and objective behavior measures. To investigate how closely smartphone data predicted MADRS scores, linear mixed models were used.</div></div><div><h3>Results</h3><div>Among enrolled participants (n=54), 35 completed the study (64.8%). PHQ-8 scores were associated with distance from home (<em>r</em>=0.173), time spent at home (<em>r</em>=−0.147) and PHQ-8 administration duration (<em>r</em>=0.151). Using demographic data and the most recent PHQ-8 scores, average root-mean-squared error for depression severity prediction across models was 1.64 with only PHQ-8 scores, 1.49 also including accelerometer and GPS data, and 1.36 also including PHQ-8 administration duration.</div></div><div><h3>Conclusion</h3><div>Smartphone sensors captured objective behavior measures in patients with IS/TIA. In predictive models, the accuracy of depression severity scores improved as measures from additional smartphone sensors were included. Future research should validate this decentralized, exploratory approach in a larger cohort. Our work is a step toward showing that real-world monitoring with active and passive data may triage patients with IS/TIA for efficient depression screening and provide digital mobility and response time endpoints.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100240"},"PeriodicalIF":0.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Technology for Informed Choices at Childbirth in Brazil: A Randomized Controlled Trial","authors":"Carmen Simone Grilo Diniz PhD ,&nbsp;Ana Carolina Arruda Franzon PhD ,&nbsp;Beatriz Fioretti-Foschi PhD ,&nbsp;Livia Sanches Pedrilio MSc ,&nbsp;Edson Amaro Jr. PhD ,&nbsp;João Ricardo Sato PhD ,&nbsp;Denise Yoshie Niy PhD","doi":"10.1016/j.mcpdig.2025.100238","DOIUrl":"10.1016/j.mcpdig.2025.100238","url":null,"abstract":"<div><h3>Objective</h3><div>To design and evaluate an information and communication intervention via a smartphone application that provides access to essential information on best practices and safety in maternity services.</div></div><div><h3>Participants and Methods</h3><div>A randomized controlled trial using a mobile application to recruit and deliver the intervention, conducted from October 31, 2020, through December 12, 2020. The study was offered to all users registered on the application who self-identified as women, with ages between 18 and 49 years, with at least 1 child, pregnant or interested in having children in the future. The primary outcome measured was increased participant engagement in seeking an active role and informed choices. Participants received information about best practices (intervention) or about diapers (control). The trial was registered on the Brazilian Clinical Trials Registry Platform, and the protocol was published according to CONSORT e-Health guidelines. Effect size was estimated by odds ratio, with CI and <em>P</em> values.</div></div><div><h3>Results</h3><div>In total, 20,608 users were invited to participate in the study; of 17,643 enrolled (85.6% of invited users), 13,969 (79.1% of enrolled participants) women completed the intervention stage and were included in the analyses; 7121 (50.9% of all women included) had up to high school level; and 5855 (41.9%) used both public and private services. The intervention group registered an increased engagement in seeking an active role or making informed choices (odds ratio, 2.06; <em>P</em>&lt;.001). The intervention proved to be highly effective for all secondary outcomes, as well.</div></div><div><h3>Conclusion</h3><div>This affordable digital technology effectively promoted awareness of safer, empowered choices in childbirth care, facilitating the translation of evidence-based, rights-based knowledge from institutional guidelines and recommendations to a broader audience.</div></div><div><h3>Trial Registration</h3><div>Brazilian Registry of Clinical Trials Identifier: RBR-3g5f9f; WHO’s Unique Trial Identifier: UTN U1111-1255-8683.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100238"},"PeriodicalIF":0.0,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}