Frontiers in digital health最新文献

{"title":"Physicians and AI in healthcare: insights from a mixed-methods study in Poland on adoption and challenges.","authors":"Ewelina Kowalewska","doi":"10.3389/fdgth.2025.1556921","DOIUrl":"10.3389/fdgth.2025.1556921","url":null,"abstract":"<p><strong>Introduction: </strong>Understanding healthcare professionals' attitudes towards artificial intelligence (AI) in medicine is crucial for improving patient care and clinical practice. This study combines a systematic review and a survey targeting Polish physicians to explore these attitudes. While many healthcare professionals express enthusiasm and readiness for AI integration, others remain skeptical due to concerns about reliability, ethical implications, and legal accountability. The systematic review highlighted AI's potential benefits, such as improved diagnostic accuracy and workflow efficiency, alongside challenges like data privacy and the need for validation in atypical scenarios.</p><p><strong>Materials and methods: </strong>This study combines insights from a systematic review and a targeted survey to assess healthcare professionals' attitudes toward AI. The survey focused on Polish physicians, a group uniquely positioned to provide insights due to their healthcare system's specific challenges.</p><p><strong>Results: </strong>The survey revealed optimism among Polish physicians (n86), with 68% ready to adopt AI tools, but underscored the necessity of tailored education and clear implementation guidelines.</p><p><strong>Discussion: </strong>This study provides valuable insights into the dual narrative of optimism and skepticism surrounding AI in healthcare, emphasizing the importance of addressing barriers to maximize its benefits globally.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1556921"},"PeriodicalIF":3.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756373","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":"Automated inflammatory bowel disease detection using wearable bowel sound event spotting.","authors":"Annalisa Baronetto, Sarah Fischer, Markus F Neurath, Oliver Amft","doi":"10.3389/fdgth.2025.1514757","DOIUrl":"10.3389/fdgth.2025.1514757","url":null,"abstract":"<p><strong>Introduction: </strong>Inflammatory bowel disorders may result in abnormal Bowel Sound (BS) characteristics during auscultation. We employ pattern spotting to detect rare bowel BS events in continuous abdominal recordings using a smart T-shirt with embedded miniaturised microphones. Subsequently, we investigate the clinical relevance of BS spotting in a classification task to distinguish patients diagnosed with inflammatory bowel disease (IBD) and healthy controls.</p><p><strong>Methods: </strong>Abdominal recordings were obtained from 24 patients with IBD with varying disease activity and 21 healthy controls across different digestive phases. In total, approximately 281 h of audio data were inspected by expert raters and thereof 136 h were manually annotated for BS events. A deep-learning-based audio pattern spotting algorithm was trained to retrieve BS events. Subsequently, features were extracted around detected BS events and a Gradient Boosting Classifier was trained to classify patients with IBD vs. healthy controls. We further explored classification window size, feature relevance, and the link between BS-based IBD classification performance and IBD activity.</p><p><strong>Results: </strong>Stratified group K-fold cross-validation experiments yielded a mean area under the receiver operating characteristic curve ≥0.83 regardless of whether BS were manually annotated or detected by the BS spotting algorithm.</p><p><strong>Discussion: </strong>Automated BS retrieval and our BS event classification approach have the potential to support diagnosis and treatment of patients with IBD.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1514757"},"PeriodicalIF":3.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11965935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782117","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":"Promoting mental health in the age of new digital tools: balancing challenges and opportunities of social media, chatbots, and wearables.","authors":"Julien Coelho, Florian Pécune, Jean-Arthur Micoulaud-Franchi, Bernard Bioulac, Pierre Philip","doi":"10.3389/fdgth.2025.1560580","DOIUrl":"10.3389/fdgth.2025.1560580","url":null,"abstract":"<p><p>The promotion of mental health is essential for global health, affecting millions with disorders such as anxiety and depression. Although stigma and discrimination hinder progress, these conditions are often preventable or manageable at minimal cost. The adoption of digital tools in mental health promotion, including telemedicine, online therapy, social media, and wearables, offers promising new avenues to address these issues. This review proposes a framework that focuses on the use of digital tools to enhance health literacy, foster behavioral change, and support sustained positive health behaviors. Platforms such as TikTok, Facebook, and Instagram can effectively disseminate health information, increase awareness, and enhance social accountability. Artificial intelligence-driven virtual agents offer personalised mental health interventions, providing motivational support and customised advice. Additionally, wearable technology (e.g., fitness trackers and smartwatches) enables real-time monitoring of vital health metrics, encouraging ongoing healthy activities. Nonetheless, these technologies introduce challenges including privacy issues, data security, and equitable access to digital resources, raising a new class of rights to protect mental privacy, guard against algorithm bias, and prevent personality-changing manipulations. The absence of human interaction in fully digital solutions also raises concerns about a lack of empathy and emotional connection. For optimal use of digital tools in mental health, integration with conventional care practices and adaptation to diverse cultural and social backgrounds are necessary. The results of this review suggest that digital tools, when carefully implemented, can significantly improve mental health outcomes by making care more accessible, tailored, and effective, especially for underserved communities.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1560580"},"PeriodicalIF":3.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11965895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-initiated humour protocol: a pilot study with an AI agent.","authors":"Abbas Edalat, Ruoyu Hu, Zeena Patel, Neophytos Polydorou, Frank Ryan, Dasha Nicholls","doi":"10.3389/fdgth.2025.1530131","DOIUrl":"10.3389/fdgth.2025.1530131","url":null,"abstract":"<p><strong>Introduction: </strong>Non-hostile humour and laughter have been known for therapeutic benefits in an individual's mental health and wellbeing. To this end, we evaluated the Self-Initiated Humour Protocol (SIHP), a new type of self-administrable laughter intervention that utilises spontaneous and self-induced laughter. Rooted in the core principles of the Self-Attachment Technique-in which an individual creates an affectional bond with their childhood self as represented by their childhood photo or personalised childhood avatar-SIHP provides an algorithmic framework for individuals to learn to laugh in a non-hostile manner and develop a sense of humour in all possible life contexts. This allows SIHP to be self-administered by interacting with an AI agent.</p><p><strong>Methods: </strong>An 8-week intervention was conducted with N = 27 adult participants. Exclusion criteria: severe depression or anxiety (PHQ-9 and GAD-7 scores above 15). Participants' measurements were collected in the areas of wellbeing, use of different humour styles, emotional self-regulation, self-compassion and psychological capital, and analysed to understand any changes over time. Measurements were taken immediately before, after the intervention, and at the 3-month follow-up. Throughout the intervention, participants were required to practise SIHP 20 min a day with the aid of an emotionally intelligent chatbot and their personalised child avatar in virtual reality (VR).</p><p><strong>Results: </strong>Analysis of results at the 3-month follow-up showed significant improvements in the primary outcome of wellbeing with large effect size ( <math><mi>r</mi> <mo>=</mo> <mn>0.92</mn></math> ), as well as a range of secondary outcomes with large effect sizes, self-compassion ( <math><mi>r</mi> <mo>=</mo> <mn>0.93</mn></math> ), use of self-enhancing humour ( <math><mi>d</mi> <mo>=</mo> <mn>0.80</mn></math> ), and emotion regulation ( <math><mi>d</mi> <mo>=</mo> <mn>0.87</mn></math> ); the results also showed improvement to participant's psychological capital with moderate effect size ( <math><mi>d</mi> <mo>=</mo> <mn>0.56</mn></math> ).</p><p><strong>Discussion: </strong>This study shows the potential for the practice of SIHP as supported by an emotionally intelligent chatbot and personalised child avatar to have medium-term positive effects, which should be validated through future randomised trials.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1530131"},"PeriodicalIF":3.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11965911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781942","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":"SympCoughNet: symptom assisted audio-based COVID-19 detection.","authors":"Yuhao Lin, Xiu Weng, Bolun Zheng, Weiwei Zhang, Zhanjun Bu, Yu Zhou","doi":"10.3389/fdgth.2025.1551298","DOIUrl":"10.3389/fdgth.2025.1551298","url":null,"abstract":"<p><p>COVID-19 remains a significant global public health challenge. While nucleic acid tests, antigen tests, and CT imaging provide high accuracy, they face inefficiencies and limited accessibility, making rapid and convenient testing difficult. Recent studies have explored COVID-19 detection using acoustic health signals, such as cough and breathing sounds. However, most existing approaches focus solely on audio classification, often leading to suboptimal accuracy while neglecting valuable prior information, such as clinical symptoms. To address this limitation, we propose SympCoughNet, a deep learning-based COVID-19 audio classification network that integrates cough sounds with clinical symptom data. Our model employs symptom-encoded channel weighting to enhance feature processing, making it more attentive to symptom information. We also conducted an ablation study to assess the impact of symptom integration by removing the symptom-attention mechanism and instead using symptoms as classification labels within a CNN-based architecture. We trained and evaluated SympCoughNet on the UK COVID-19 Vocal Audio Dataset. Our model demonstrated significant performance improvements over traditional audio-only approaches, achieving 89.30% accuracy, 94.74% AUROC, and 91.62% PR on the test set. The results confirm that incorporating symptom data enhances COVID-19 detection performance. Additionally, we found that incorrect symptom inputs could influence predictions. Our ablation study validated that even when symptoms are treated as classification labels, the network can still effectively leverage cough audio to infer symptom-related information.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1551298"},"PeriodicalIF":3.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11936986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722862","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 on women's body satisfaction of exposure to postpartum imagery on social media.","authors":"Megan L Gow, Maddison Henderson, Amanda Henry, Lynne Roberts, Heike Roth","doi":"10.3389/fdgth.2025.1379337","DOIUrl":"10.3389/fdgth.2025.1379337","url":null,"abstract":"<p><strong>Background: </strong>Social networking sites may be a convenient, accessible and low-cost option for delivering health information at scale to postpartum women. However, social media use is associated with decreased body satisfaction and may contribute to psychological ill-health. Our study aimed to determine whether exposure to body-focused imagery, typical of imagery targeting postpartum women on Instagram, is associated with a reduction in state body satisfaction and state body appreciation. Secondly, we aimed to determine whether including postpartum-health-focused imagery, in conjunction with body-focused imagery, is associated with improving state body satisfaction/appreciation, compared with no postpartum health content.</p><p><strong>Methods: </strong>A single blinded quasi-experimental survey study, recruiting women who had given birth in the previous 2-years, asked participants about key demographic information, social media use and assessed thin-ideal internalization and media appearance pressures using validated tools. Participants were then exposed to either (1) 15 body-focused images of women with a thin-average level of adiposity; (2) as per (1) PLUS 5 postpartum-health-focused images; or (3) as per (1) PLUS 15 postpartum-health-focused images. State body satisfaction/appreciation were assessed before and after image exposure.</p><p><strong>Results: </strong>State body satisfaction/appreciation did not change from pre- to post-image exposure in any groups and measures were not different between groups at any time point.</p><p><strong>Discussion: </strong>Short-term exposure to body-focused imagery typical of Instagram content targeting postpartum women may not alter state body satisfaction or state body appreciation. Furthermore, incorporating postpartum-health-focused imagery did not alter results. Further research investigating whether an intervention providing health information to postpartum women via social media platforms improves health outcomes may be warranted.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1379337"},"PeriodicalIF":3.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11937002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722888","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":"Responsible design of an AI system for health behavior change-an ethics perspective on the participatory design process of the STAR-C digital coach.","authors":"Helena Lindgren, Kristina Lindvall, Linda Richter-Sundberg","doi":"10.3389/fdgth.2025.1436347","DOIUrl":"10.3389/fdgth.2025.1436347","url":null,"abstract":"<p><strong>Introduction: </strong>The increased focus on the ethical aspects of artificial intelligence (AI) follows the increased use in society of data-driven analyses of personal information collected in the use of digital applications for various purposes that the individual is often not aware of. The purpose of this study is to investigate how values and norms are transformed into design choices in a participatory design process of an AI-based digital coaching application for promoting health and to prevent cardiovascular diseases, where a variety of expertise and perspectives are represented.</p><p><strong>Method: </strong>A participatory design process was conducted engaging domain professionals and potential users in co-design workshops, interviews and observations of prototype use. The design process and outcome was analyzed from a responsible design of AI systems perspective.</p><p><strong>Results: </strong>The results include deepened understanding of the values and norms underlying health coaching applications and how an AI-based intervention could provide person-tailored support in managing conflicting norms. Further, the study contributes to increased awareness of the value of participatory design in achieving value-based design of AI systems aimed at promoting health through behavior change, and the inclusion of social norms as a design material in the process.</p><p><strong>Conclusion: </strong>It was concluded that the relationship between the anticipated future users and the organization(s) or enterprises developing and implementing the health-promoting application is directing which values are manifested in the application.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1436347"},"PeriodicalIF":3.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934961/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712295","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":"Editorial: Bench to bedside: AI and remote patient monitoring.","authors":"Ramin Ramezani, Saam Iranmanesh, Arash Naeim, Peyman Benharash","doi":"10.3389/fdgth.2025.1584443","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1584443","url":null,"abstract":"","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1584443"},"PeriodicalIF":3.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11933033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712231","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":"Artificial intelligence in nursing: an integrative review of clinical and operational impacts.","authors":"Salwa Hassanein, Rabie Adel El Arab, Amany Abdrbo, Mohammad S Abu-Mahfouz, Mastoura Khames Farag Gaballah, Mohamed Mahmoud Seweid, Mohammed Almari, Husam Alzghoul","doi":"10.3389/fdgth.2025.1552372","DOIUrl":"10.3389/fdgth.2025.1552372","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Advances in digital technologies and artificial intelligence (AI) are reshaping healthcare delivery, with AI increasingly integrated into nursing practice. These innovations promise enhanced diagnostic precision, improved operational workflows, and more personalized patient care. However, the direct impact of AI on clinical outcomes, workflow efficiency, and nursing staff well-being requires further elucidation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This integrative review synthesized findings from 18 studies published through November 2024 across diverse healthcare settings. Using the PRISMA 2020 and SPIDER frameworks alongside rigorous quality appraisal tools (MMAT and ROBINS-I), the review examined the multifaceted effects of AI integration in nursing. Our analysis focused on three principal domains: clinical advancements and patient monitoring, operational efficiency and workload management, and ethical implications.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The review demonstrates that AI integration in nursing has yielded substantial clinical and operational benefits. AI-powered monitoring systems, including wearable sensors and real-time alert platforms, have enabled nurses to detect subtle physiological changes-such as early fever onset or pain indicators-well before traditional methods, resulting in timely interventions that reduce complications, shorten hospital stays, and lower readmission rates. For example, several studies reported that early-warning algorithms facilitated faster clinical responses, thereby improving patient safety and outcomes. Operationally, AI-based automation of routine tasks (e.g., scheduling, administrative documentation, and predictive workload classification) has streamlined resource allocation. These efficiencies have led to a measurable reduction in nurse burnout and improved job satisfaction, as nurses can devote more time to direct patient care. However, despite these benefits, ethical challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. These issues underscore the need for robust ethical frameworks and targeted AI literacy training within nursing curricula.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;This review demonstrates that AI integration holds transformative potential for nursing practice by enhancing both clinical outcomes and operational efficiency. However, to realize these benefits fully, it is imperative to develop robust ethical frameworks, incorporate comprehensive AI literacy training into nursing education, and foster interdisciplinary collaboration. Future longitudinal studies across varied clinical contexts are essential to validate these findings and support the sustainable, equitable implementation of AI technologies in nursing. Policymakers and healthcare leaders must prioritize investments in AI solutions that complement the expertise of nursing professionals w","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1552372"},"PeriodicalIF":3.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694586","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":"Effect of a digital blood pressure coach on hypertension management in primary care practices-a pragmatic, randomised controlled trial.","authors":"Christian Beger, Dominik Rüegger, Anna Lenz, Steffen Wagner, Kai Martin Schmidt-Ott, Dirk Volland, Florian P Limbourg","doi":"10.3389/fdgth.2025.1516600","DOIUrl":"10.3389/fdgth.2025.1516600","url":null,"abstract":"<p><strong>Importance: </strong>Smartphone medical applications (apps) may improve blood pressure (BP) control in the primary care setting in patients with hypertension. However, real-world evidence from primary care is largely lacking.</p><p><strong>Objective: </strong>To analyse, in primary care practices in Germany, the effect of a smartphone app on systolic BP compared to standard of care.</p><p><strong>Design: </strong>A pragmatic, non-blinded randomized controlled trial with patients with a diagnosis of hypertension was conducted across 23 general practices in Germany, with a follow-up period of 3 months. Recruitment occurred from January 2022 to May 2023.</p><p><strong>Intervention: </strong>The intervention group received access to the Manoa app, a smartphone coach integrating guideline-compliant home BP monitoring and lifestyle-coaching. All study participants received standard treatment for arterial hypertension at the discretion of the treating physician.</p><p><strong>Main outcomes: </strong>The primary outcome was office systolic BP (oSBP) after 90-150 days in participants with uncontrolled hypertension (oSBP ≥140 mmHg). Secondary outcomes included changes in systolic and diastolic BP, BP control and adherence to home blood pressure monitoring.</p><p><strong>Results: </strong>A total of 606 participants from 23 general practices were randomized, after data clearance and review, 249 participants were assigned to the control group and 259 to the intervention group for analysis. The mean age (SD) of participants in the intervention group was 55.9 (12.9) years. At baseline, participants with uncontrolled hypertension had a mean oSBP (SD) of 152.6 (14.2) mmHg in the intervention group (<i>n</i> = 162) and 152.6 (14.1) mmHg in the control group (<i>n</i> = 147). After 120 ± 14 days, oSBP decreased to 137.4 (14.4) mmHg in the intervention group and to 137.7 (14.5) mmHg in the control group, with a between-group mean difference of -0.2 mmHg [95% CI (-3.9,3.5); <i>P</i> =.9]. At the follow-up appointment, 69.1% of participants in the intervention group submitted a BP-diary, compared to 36.1% in the control group [OR = 3.95; 95% CI (2.73,5.72); <i>P</i> = <0.001].</p><p><strong>Conclusions and relevance: </strong>Participants with uncontrolled hypertension randomized to an app in primary care achieved similar decreases in systolic BP but higher adherence to home BP monitoring compared to standard care. In this open-label, pragmatic trial, variability in hypertension management strategies and limited standardization across practices may have confounded the precise evaluation of digital intervention benefits.</p><p><strong>Clinical trial registration: </strong>ClinicalTrials.gov, identifier, (DRKS00027964).</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1516600"},"PeriodicalIF":3.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11925922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694587","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}