Frontiers in digital health最新文献

{"title":"Editorial: The scale-up and sustainability of digital health interventions in low- and middle-income settings.","authors":"Bassey Ebenso, Eve Namisango, Ibukun-Oluwa Abejirinde, Matthew J Allsop","doi":"10.3389/fdgth.2025.1634223","DOIUrl":"10.3389/fdgth.2025.1634223","url":null,"abstract":"","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1634223"},"PeriodicalIF":3.2,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12497710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245884","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":"TRACE: applying AI language models to extract ancestry information from curated biomedical literature.","authors":"Alison M Veintimilla, Chintan K Acharya, Connie J Mulligan, Ruogu Fang, Erika Moore","doi":"10.3389/fdgth.2025.1608370","DOIUrl":"10.3389/fdgth.2025.1608370","url":null,"abstract":"<p><strong>Introduction: </strong>Ancestry reporting is essential to ensure transparency and proper representation in biomedical studies. However, manually extracting this information from study texts is time-consuming and inefficient. In this paper, we present TRACE (Tool for Researching Ancestry and Cell Extraction), powered by GPT-4 and web-crawling, to automate ancestry identification by detecting cell lines or cultures in texts and tracing their ancestry.</p><p><strong>Methods: </strong>TRACE extracts cell lines and primary cultures from research articles and follows web sources to determine their ancestry. We compared TRACE's outputs to a manually generated database to confirm its performance in identifying and verifying ancestry information.</p><p><strong>Results: </strong>The results reveal an overrepresentation of European/White samples and significant underreporting. TRACE enables large-scale, systematic ancestry analysis-a valuable resource for researchers and agencies assessing biases in sample selection.</p><p><strong>Conclusions: </strong>As an open-source tool, TRACE it facilitates broader use to evaluate and improve ancestry representation in biomedical research.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1608370"},"PeriodicalIF":3.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234257","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":"Increasing access to care through digital health for the Medicaid population: a novel community case study.","authors":"Melinda Cooling, Colleen J Klein, Matthew D Dalstrom, Roopa Foulger, Jennifer Junis, Jonathan A Handler","doi":"10.3389/fdgth.2025.1524590","DOIUrl":"10.3389/fdgth.2025.1524590","url":null,"abstract":"<p><p>There is a growing consensus among healthcare professionals and policymakers that the way healthcare has historically been provided within the United States is insufficient to meet the needs of the population. The incidence and prevalence of many chronic diseases, coupled with the challenges associated with accessing prenatal care, are notable across the country and globally. In response to this problem OSF HealthCare and four federally qualified health centers partnered together to reimagine how health care can be delivered to underserved populations. This case study provides a practical perspective on how care delivery is enhanced, delivered, and improved through use of digital technologies to expand access to care and chronic disease management in the Medicaid population. Through the formation of the Medicaid Innovation Collaborative, which is partially funded by the Illinois Department of Health and Family Services, digital health programs tailored to individual patient needs and supported by remote and in-person digital health navigators (DHNs), are provided with 24/7/365 access to care from a diverse team of healthcare professionals. In this article, we describe the essential program elements, design, and implementation of four novel programs. While developing digital care solutions for adult Medicaid recipients across the state has been challenging, our work illustrates the feasibility of such an endeavor. To date, we have outreached to over 418,037 patients, and enrolled 38,964 in our diverse programs that include, but are not limited to, helping patients managing chronic disease, increasing access to prenatal care, offering support for health literacy and wellness, and screening for the social determinants of health.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1524590"},"PeriodicalIF":3.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234232","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":"DISCOVER: a Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of human behavior.","authors":"Tobias Hallmen, Dominik Schiller, Antonia Vehlen, Steffen Eberhardt, Tobias Baur, Daksitha Withanage Don, Wolfgang Lutz, Elisabeth André","doi":"10.3389/fdgth.2025.1638539","DOIUrl":"10.3389/fdgth.2025.1638539","url":null,"abstract":"<p><p>Understanding human behavior is a fundamental goal of social sciences, yet conventional methodologies are often limited by labor-intensive data collection and complex analyses. Computational models offer a promising alternative for analyzing large datasets and identifying key behavioral indicators, but their adoption is hindered by technical complexity and substantial computational requirements. To address these barriers, we introduce <i>DISCOVER</i>, a modular and user-friendly software framework designed to streamline computational data exploration for human behavior analysis. <i>DISCOVER</i> democratizes access to state-of-the-art models, enabling researchers across disciplines to conduct detailed behavioral analyses without extensive technical expertise. In this paper, we are showcasing <i>DISCOVER</i> using four modular data exploration workflows that build on each other: Semantic Content Exploration, Visual Inspection, Aided Annotation, and Multimodal Scene Search. Finally, we report initial findings from a user study. The study examined <i>DISCOVER</i>'s potential to support prospective psychotherapists in structuring information for treatment planning, i.e. case conceptualizations.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1638539"},"PeriodicalIF":3.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492635/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234285","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":"Prevention of sudden unexpected postnatal collapse in wellbeing newborns by remote digital health technologies.","authors":"Massimo Berger, Adalberto Brach Del Prever, Michele Mario Calvo, Roberto Bellino, Davide Gallina, Fabio Stefano Timeus, Fabrizio Bogliatto","doi":"10.3389/fdgth.2025.1598541","DOIUrl":"10.3389/fdgth.2025.1598541","url":null,"abstract":"<p><strong>Introduction: </strong>To prevent the Sudden Unexpected Postnatal Collapse (SUPC) this approach was carried out. SUPC is a rare and devastating event for the child and their family. Currently, no diagnostic prediction model is available to calculate the individual newborn risk.</p><p><strong>Patient and methods: </strong>To prevent SUPC, the Department of Maternal and Child Health at ASLTO4 in Piedmont, Northern Italy, has implemented wireless cardiopulmonary monitoring for all newborns during the first 24 h of life, starting on June 10th, 2023, to December 31st, 2024. The study involved approximately 2,000 newborns from three Spoke hospitals in Northern Italy. The aim of the study was to evaluate the feasibility of wireless monitoring in a large series of newborns.</p><p><strong>Results: </strong>On more than 2,000 newborns, we have seen parental refusal in only two cases. The system was well accepted by the families after adequate explanation of the monitoring modalities and its meaning. The wireless system has in no way hindered the skin-to-skin moment nor delayed the time of attachment to the breast and the usual neonatal screening procedures. The introduction of this new technology has brought increased serenity to parents, especially in situations of severe tiredness after troubled births or after cesarean delivery. As a very preliminary results in 2,250 newborns the monitoring system detected various pathological events, in particular two cases of SUPC which were promptly resuscitated without subsequent neurological sequelae.</p><p><strong>Conclusions: </strong>We report on our proof-of-concept innovative digital approach to intercept SUPCs as soon as possible. Through this study we want to demonstrate that it is possible to carry out large-scale multicenter monitoring, without interfering with breast attachment and the initial mother-infant relationship. The limitations of the study mainly concern the fact that this monitoring was carried out on term or late pre-term infants. This was due to the unavailability of a neonatal intensive care (TIN) within our hospitals and therefore severe preterm children were born or transferred early to a third level hospital.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1598541"},"PeriodicalIF":3.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234219","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":"Impact of integrating guidelines into an antimicrobial stewardship smartphone application on outpatient antibiotic prescribing: a segmented interrupted time series analysis.","authors":"Ahmed A Sadeq, Laila Z Alhaj Ali, Jinan M Shamseddine, Barbara R Conway, Stuart E Bond, Rizwan Ali, William J Lattyak, Zahir Osman Eltahir Babiker, Mamoon A Aldeyab","doi":"10.3389/fdgth.2025.1647528","DOIUrl":"10.3389/fdgth.2025.1647528","url":null,"abstract":"<p><strong>Introduction: </strong>Antimicrobial stewardship (AMS) smartphone applications (apps) have been adopted to promote better antimicrobial prescribing practices. We aimed to evaluate the impact of incorporating an app on AMS metrics and adherence to a local antimicrobial guideline in an outpatient setting.</p><p><strong>Methods: </strong>A quasi-experimental, segmented interrupted time series design was used, involving three study phases (pre-intervention: 1st January 2020 to 31st December 2021; implementation: 1st January 2022 to 31st December 2022, and post-intervention: 1st January 2023 to 30th June 2024) in a hospital outpatient setting. The effect of introducing an AMS app incorporating local antimicrobial guidelines on AMS outcomes was measured.</p><p><strong>Results: </strong>A total of 24,424 patients were identified. As per the most simple model, the amounts of the following antibiotics, expressed as defined daily dose (DDD) per 100 patient visits, increased significantly during the post-intervention phase: azithromycin (co-efficient 0.297, <i>p</i> = 0.007), co-amoxiclav (co-efficient 2.608, <i>p</i> = 0.042), and nitrofurantoin (co-efficient 0.908, <i>p</i> = 0.003). The trend in fosfomycin use decreased significantly in the post-intervention phase (co-efficient -0.23., <i>p</i> < 0.001). Guideline adherence increased significantly after implementing the AMS app (trend change co-efficient 0.011, <i>p</i> < 0.001). These changes in antibiotic prescribing represent improved guideline adherence, and are aligned with WHO AWaRe categorisation recommendations.</p><p><strong>Conclusion: </strong>The app improved the utilization of antibiotic prescribing by increasing adherence to local antimicrobial guidelines, affirming its utility in augmenting AMS in outpatient settings.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1647528"},"PeriodicalIF":3.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208480","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":"Development of a novel artificial intelligence algorithm for interpreting fetal heart rate and uterine activity data in cardiotocography.","authors":"Rohit Pardasani, Renee Vitullo, Sara Harris, Halit O Yapici, John Beard","doi":"10.3389/fdgth.2025.1638424","DOIUrl":"10.3389/fdgth.2025.1638424","url":null,"abstract":"<p><strong>Introduction: </strong>Cardiotocography (CTG) assesses fetal well-being through measurements of fetal heart rate (FHR) and uterine activity (UA). Manual visual assessment of fetal tracings is variable due to the subjective nature of their interpretation. Artificial intelligence (AI) using automatic signal processing may be leveraged to support consistent, comprehensive interpretations. This study demonstrated the development and training of a novel AI algorithm that analyzes and interprets certain clinical events and parameters calculated during labor to assist with clinical decisions.</p><p><strong>Methods: </strong>Fetal tracings sourced from 19 birthing centers through a US-based healthcare delivery organization were clinically interpreted, labeled, quality checked, and ratified by clinicians to be included in the study. The algorithm using deep learning and rule-based techniques was developed to identify segments of interest (accelerations, decelerations, and contractions). A three parallel one-dimensional Unet design with two inputs (FHR and UA) and one channel output each (for accelerations, decelerations, and contractions) was selected as the final architecture. Algorithm performance was evaluated through recall (sensitivity), precision, <i>F</i>1 score, and duration and numerical ratios.</p><p><strong>Results: </strong>A total of 133,696 patient files were used to create fetal tracings. After the exclusion, labeling, and ratification processes, the final datasets included 1,600 tracings for training, 421 for validation, and 591 for testing. The model provided promising performance and achieved <i>F</i>1 scores of 0.803 for accelerations, 0.520 for decelerations, and 0.868 for contractions on the final test set, with a 91.5% predicted baseline accuracy (difference of ≤5 bpm) compared to clinician interpretation.</p><p><strong>Conclusion: </strong>This study demonstrates the successful development of a novel AI algorithm utilizing FHR and UA data to analyze and interpret fetal tracing events and parameters. The algorithm may have potential to enhance patient care by supporting bedside clinician CTG interpretation.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1638424"},"PeriodicalIF":3.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208437","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":"Beyond weight loss: digital therapeutic for behavioral change and psychological well-being for individuals with overweight and obesity in a primary healthcare setting-A randomized controlled pilot study.","authors":"Marthe Isaksen Aukan, Maria Arlèn Larsen, Tone Iren Melan, Øyvind Olav Salvesen","doi":"10.3389/fdgth.2025.1671649","DOIUrl":"10.3389/fdgth.2025.1671649","url":null,"abstract":"<p><strong>Objective: </strong>Mobile health (mHealth) through digital therapeutics (DTx) offer a promising approach to obesity management. This study evaluated the effectiveness of the Lifeness DTx for obesity care and its effect on anthropometrics, reward-related eating behaviors and quality of life in individuals with overweight and obesity within a community-based healthcare setting.</p><p><strong>Methods: </strong>A 12-week randomized controlled trial was conducted. Adults (BMI ≥ 27 kg/m², and central obesity) were recruited from municipal Healthy Life Centers in Norway. The intervention group (IG) received standard care plus full DTx app with program functionality and digital follow-up, whereas the control group (CG) received standard care with limited app functions and no DTx program. Outcome variables were measured at baseline and after 12 weeks.</p><p><strong>Results: </strong>No significant changes in body weight, or differences between groups were observed at W12. The IG showed reductions in waist circumference (-3.4 cm, <i>p</i> = 0.008, <i>d</i> = -0.926), waist-to-height ratio (-0.02, <i>p</i> = 0.008, <i>d</i> = -0.929), improvements on hedonic eating behavior, indicated by reduced disinhibition (-1.6, <i>p</i> = 0.013, <i>d</i> = -0.907), as well as increased quality of life (+5.0, <i>p</i> = 0.019, <i>d</i> = 0.899). Both groups increased self-esteem (IG +9.8, <i>p</i> = 0.018, <i>d</i> = 0.911, and CG +12, <i>p</i> = 0.050, <i>d</i> = 0.838).</p><p><strong>Conclusion: </strong>The DTx intervention was associated with improvements in central adiposity, reward-related eating behaviors, and psychological well-being beyond weight loss. These findings provide preliminary evidence that digital therapeutics may represent a feasible and scalable approach to support personalized obesity care in primary healthcare settings. Larger, adequately powered trials are needed to confirm these results.</p><p><strong>Clinical trial registration: </strong>clinicaltrials.gov, identifier NCT06667843 (Initial Release: 10/15/2024).</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1671649"},"PeriodicalIF":3.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208520","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":"Evaluating CFIR 2.0 in identifying digital twin implementation challenges in healthcare: bridging the dichotomy between engineering and healthcare communities.","authors":"Md Doulotuzzaman Xames, Taylan G Topcu, Sarah H Parker, Vivian Zagarese, John W Epling","doi":"10.3389/fdgth.2025.1611225","DOIUrl":"10.3389/fdgth.2025.1611225","url":null,"abstract":"<p><strong>Background: </strong>Digital twin (DT) technology holds significant promise for healthcare systems (HSs) due to real-time monitoring based on streaming operational data and <i>a priori</i> analysis capabilities without interrupting clinical workflows. However, the sociotechnical complexity of HSs presents challenges for effective DT implementation. A dichotomy also exists between the engineering and implementation science (IS) communities regarding DT implementation challenges. This study assesses the efficacy of the updated Consolidated Framework for Implementation Research (CFIR 2.0) in identifying DT implementation challenges, aiming to bridge the knowledge gap between IS and DT communities.</p><p><strong>Methods: </strong>This study presents findings from a DT implementation case study in a family medicine clinic, an operational healthcare microsystem. It adopts CFIR 2.0 to guide semi-structured interviews with four key stakeholder groups (e.g., family medicine specialists, engineers, organizational psychologists, and implementation scientists). Participants (<i>N</i> = 8) were purposively sampled based on their roles in DT implementation. Thematic coding categorized interview data into seven themes: technological, data-related, financial and economic, regulatory and ethical, organizational, operational, and personnel. Thematic data were then cross-analyzed with challenges documented in DT literature to assess how effectively CFIR 2.0 identifies DT implementation challenges.</p><p><strong>Results: </strong>Challenges were grouped into three categories: (i) shared challenges captured by both IS and DT communities, (ii) CFIR 2.0-identified challenges overlooked in DT literature, and (iii) challenges documented in DT research but not captured through CFIR 2.0-guided interviews. While there was strong overlap between the communities, a formidable gap also remains. CFIR 2.0 effectively identified a diverse set of issues-predominantly in organizational, financial, and operational themes-including many overlooked by the DT community. However, it was less effective in capturing technological and data-related barriers critical to DT performance, such as modeling, real-time synchronization, and sensor reliability.</p><p><strong>Conclusions: </strong>CFIR 2.0 effectively identifies organizational and operational barriers to DT implementation in healthcare but falls short in addressing technological and data-related complexities. This study highlights the need for interdisciplinary collaboration for the successful transition of emerging DT technologies into practice to maximize their impact on HS efficiency and patient outcomes.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1611225"},"PeriodicalIF":3.2,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201991","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":"What are the functionalities and features of mobile health record apps supporting persons experiencing social exclusion? A systematic literature review.","authors":"Felicien Izaturwanaho, Marie E Ward, Clíona Ní Cheallaigh, Maeve Moran, Geraldine Fitzgerald, David Mockler, Una Geary, Siobhán Corrigan","doi":"10.3389/fdgth.2025.1629289","DOIUrl":"10.3389/fdgth.2025.1629289","url":null,"abstract":"<p><strong>Background: </strong>Research into mobile health record apps has focused on narrow outcomes, such as medication adherence for persons experiencing chronic conditions. However, no review has examined their use in the context of social exclusion. Persons experiencing social exclusion (PESE) face complex health needs, limited healthcare access, and increased exposure to traumatic life experiences. It is imperative to consider a trauma-informed and integrated care approaches when developing an app for them, and they should be involved as key stakeholders to ensure equitable care. This review examined these apps' functionalities and features that support PESE in relation to their reported outcomes and the delivery of a trauma-informed and/or integrated care.</p><p><strong>Methods: </strong>A systematic search of ten databases: Web of Science Core Collection, Medline, PsycINFO, CINAHL, Cochrane, Embase, Scopus, ProQuest Dissertations and Theses A&I, Lenus and OpenGrey International were undertaken, and was supplemented with non-indexed and grey literature. Searches were undertaken in April 2024 in English with no date limit, and used the PRISMA 2020 guidelines. Studies were deemed eligible if they met the SPIDER framework criteria.</p><p><strong>Results: </strong>One thousand three hundred and thirty-two papers were found eligible for the review, of which eleven qualified for inclusion following screening and quality assessment using QATSDD and MMAT tools. Four themes were found (supporting integrated and connected care; enhancement of user engagement and care coordination; improving data accuracy and access to care; and provision of ongoing monitoring and feedback) related to apps' functionalities and features, which in turn were linked to reported outcomes. Although a few of these apps' functionalities and features were aligned with the six principles of trauma-informed care, none of them were implemented considering a trauma-informed care and/or integrated care.</p><p><strong>Conclusion: </strong>This review provided insights into the complexities of implementing a mobile health record app for PESE. However, limited available data restricted a comprehensive understanding of these apps' functionalities and features in their specific implementation settings in relation to their reported outcomes. Next steps include translating these findings into survey and interview questions to identify end-user requirements for developing an app for PESE from a trauma-informed perspective to promote integrated care.</p><p><strong>Systematic review registration: </strong>PROSPERO CRD42024535090.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1629289"},"PeriodicalIF":3.2,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202058","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}