Frontiers in digital health最新文献

{"title":"Offering in-home video visits: a descriptive national survey of interprofessional clinicians across Veterans Health Administration.","authors":"Emily E Metcalf, Da Jung Chang, Steven D Shirk, Lauren R Moo, Megan E Gately","doi":"10.3389/fdgth.2026.1912233","DOIUrl":"10.3389/fdgth.2026.1912233","url":null,"abstract":"<p><strong>Introduction: </strong>Utilization of telehealth has greatly expanded in recent years to ensure access to care during uncertain times such as the COVID pandemic. This included offering telehealth to patients not previously considered ideal candidates for telehealth. In-home synchronous video telehealth has unique knowledge and technical requirements and barriers when compared to other modalities like telephone or asynchronous websites and applications. The complexity of in-home video may limit its reach to certain patients, including older adults and those with complex medical needs, thereby exacerbating care disparities. While clinicians may be critical decision-makers in determining use of in-home video, little is known about how clinicians decide to whom video should be offered. To ensure equitable distribution of in-home video to patients who need it most, this study investigates clinician decision-making around the initial offer of in-home video and what factors influence their decision.</p><p><strong>Methods: </strong>A web-based national survey of Department of Veterans Health Affairs (VHA) interprofessional clinicians about decision-making to offer in-home video was conducted between March and April 2025. The analytic sample includes clinicians who used in-home video during the previous 12 months. Items were iteratively developed with input from interprofessional clinician subject matter experts, and gathered, 1) respondent characteristics, 2) clinical experience and video utilization details, 3) patient, clinical, and administrative factors, benefits, and barriers, and 4) video facilitators.</p><p><strong>Results: </strong>Of 11,416 respondents (5.5% response rate), most were female (68.31%), and the top three clinical disciplines included Social Workers (21.84%), Physicians (18.5%), and Nursing Professionals (15.22%). The top three clinical services delivered using in-home video were routine assessments or examinations (49.18%), assessments or examinations of new or acute issues (41.62%), and ongoing chronic disease management (40.92%). When clinicians were asked to rate the quality of care delivered via in-home video compared to in the clinic, the majority reported that they feel the quality is about the same or better when using video (84.20%). The top three patient factors that clinicians consider when deciding to offer in-home video included patient willingness and/or preference for video (77.58%), travel barriers including inclement weather, lack of transportation, traffic (76.35%), and rurality or distance from a VHA facility (74.43%). Patient factors selected the least included motor and sensory function (2,504, 21.93%), medical acuity (2,776, 24.32%), and age (3,107, 27.22%). The top three clinical and administrative factors identified as making the clinician more likely to offer in-home video included the convenience of using video to the clinician's workflow (55.23%), high patient acuity or the need to be seen quick","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1912233"},"PeriodicalIF":5.2,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13617108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076881","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":"Migraine care in the digital era: social listening insights and neurologists' perspectives from Türkiye.","authors":"Derya Uluduz, Pınar Yalınay Dikmen, Burcin Yazici, Aynur Ozge","doi":"10.3389/fdgth.2026.1891640","DOIUrl":"10.3389/fdgth.2026.1891640","url":null,"abstract":"<p><strong>Objective: </strong>To explore migraine-related concerns expressed in user-generated digital entries from a Turkish online health platform and contextualize them through neurologists' expert perspectives. Background: Digital platforms may shape symptom interpretation, treatment expectations, and communication; however, social-listening findings require cautious interpretation given platform-related and methodological limitations.</p><p><strong>Methods: </strong>This exploratory study combined secondary interpretation of a pre-existing proprietary Kimola social-listening report with a survey of 10 neurologists with headache expertise. The report included analytical outputs from 2,500 migraine-related user-generated entries posted on Doktorsitesi.com between 2007 and 2023. Because raw entries, preprocessing records, and coding files were unavailable to the authors, findings were interpreted descriptively as source-report outputs. Neurologist responses were summarized as raw counts and narratively contextualized with entry-derived themes, without formal mixed-methods integration.</p><p><strong>Results: </strong>According to the source report, entries were categorized into four migraine-journey stages: pre-treatment, treatment phase, disease experience, and post-treatment. These stages reflected symptom interpretation and diagnostic uncertainty, treatment effectiveness and safety concerns, broader disease burden, and discontinuation or interest in non-oral, procedural, or complementary options after treatment. In the survey, 7 of 10 neurologists agreed or strongly agreed that many people with migraine use social media for migraine-related information, and 9 of 10 agreed or strongly agreed that misinformation complicates physician-patient communication.</p><p><strong>Conclusions: </strong>Among digitally engaged platform users, migraine-related entries revealed unmet information needs and treatment-related concerns. Neurologists highlighted the need for expert-led, evidence-based digital education. Although exploratory and not statistically generalizable, these findings may inform communication and digital education strategies in migraine care.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1891640"},"PeriodicalIF":5.2,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13617366/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076922","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":"Rethinking AI in clinical decision support: a framework for reciprocal human-AI interaction.","authors":"Colin John Greengrass","doi":"10.3389/fdgth.2026.1887161","DOIUrl":"10.3389/fdgth.2026.1887161","url":null,"abstract":"<p><p>Clinical AI systems increasingly match or exceed clinicians on some diagnostic benchmarks. Yet this reveals little about how AI output functions within clinical reasoning or how repeated reliance affects clinicians' independent capability. This paper proposes Bounded Reciprocal Adaptation for Clinician Engagement (BRACE), a framework for AI-enabled clinical decision support centred on the clinician-AI interaction rather than the model output. BRACE links three levels of support. Within a case, it makes uncertainty visible, preserves reasoning state and selectively prompts verification. Across encounters, it supports the development and maintenance of independent reasoning through bounded adaptation and formative review. Institutionally, it separates developmental interaction from evaluative use and routes clinician-initiated reports of AI error or contextual mismatch through governed review. Its governing principle is to provide sufficient support for safe care while preserving the work required for continued independent development. Adaptation is bounded in what the system may infer or modify and how interactional data may be used. These bounds are essential because a reasoning record has developmental value only if clinicians can reason freely, without withholding uncertainty, doubt or provisional judgements out of concern that they may later be used evaluatively. These provisions address design inversion, in which support intended to reduce cognitive vulnerability instead amplifies it: acutely through overreliance and cumulatively through loss or distortion of independent capability. Repeated cognitive and metacognitive offloading to AI may disrupt illness-script formation, maintenance or accuracy, resulting respectively in script-level forms of never-skilling, deskilling or mis-skilling. When AI displaces the discrimination needed to represent an unfamiliar presentation, the corresponding illness script may not form; this risk may be concentrated in ambiguous cases that also attract greater support. The central hypothesis is that the amount and type of cognitive and metacognitive work preserved during a BRACE-mediated encounter should predict independent capability subsequently demonstrated without AI. Evaluation pairs Tier 1 interactional measures derived from ordinary use with Tier 2 external anchors of later AI-off performance. Within-clinician longitudinal analyses test whether preserved cognitive and metacognitive work predicts those outcomes, while inference-drift audits test whether adaptation remains within its declared bounds.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1887161"},"PeriodicalIF":5.2,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13612526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066502","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 the perceived AI-physician consistency and trust scale for cardiology: a Delphi-based study.","authors":"Yun Yan, Zhiping Wang, Xueting Wang, Yuxia Zhang, Ping Zhang","doi":"10.3389/fdgth.2026.1880574","DOIUrl":"10.3389/fdgth.2026.1880574","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Large language models (LLMs) have demonstrated considerable potential in cardiovascular diagnostic assistance and clinical decision support. A critical safety-related question, however, remains poorly characterised: how patients subjectively perceive the consistency between artificial intelligence (AI) and physician diagnoses, and how they redistribute trust between the two when their outputs diverge. The costs of miscalibrated trust are ultimately borne downstream by emergency and intensive care services, a pathway typified by decompensating heart failure, in which algorithm-reassured delay converts an outpatient consultation into an intensive care admission. Recurrent clinical antecedents include patient anchoring on the most benign diagnosis offered by AI, and consultation-room friction, often compounded by concealed AI use, that quietly erodes adherence to physician advice. No standardised measurement instrument currently addresses this gap. To develop and content-validate an initial item pool for the Perceived AI-Physician Consistency and Trust Scale for Cardiology (PACT) for use in cardiology outpatient settings, as a preliminary step toward a standardised instrument for evaluating the safety of human-machine interaction in cardiovascular digital health.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We pre-specified four domains: AI-physician perceived consistency, AI competence trust, AI benevolence trust, and physician reference trust. An initial pool of 10 candidate items was generated. Ten experts from cardiology, medical informatics, and nursing psychology participated in three rounds of Delphi consultation; Rounds 1 and 2 screened and revised the item pool, and Round 3 re-administered the full 12-item pool using the same I-CVI and clarity instrument, with particular focus on confirming the three items whose domain assignment or response format had changed after Round 2. Items were screened using the item-level content validity index (I-CVI) and a 5-point language clarity rating, with retention thresholds of I-CVI ≥ 0.78 and mean clarity score ≥ 3.5. Kendall's coefficient of concordance (W) quantified convergence of expert opinion.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Response rates were 100% in all three rounds, and the mean expert authority coefficient was Cr = 0.86. In Round 1, items Q6 and Q8 yielded I-CVIs of 0.70 and mean clarity scores of 3.3 and 3.5 respectively; both were reconstructed after panel deliberation. The remaining eight items had I-CVIs of 0.90-1.00. Two new items were added on the basis of expert recommendations, producing a 12-item revised pool. In Round 2, all 12 items achieved I-CVIs of 0.90-1.00 and mean clarity scores of 4.2-4.9. Kendall's W for item relevance was 0.35 (&lt;i&gt;P&lt;/i&gt; = 0.002) in Round 1 and 0.41 (&lt;i&gt;P&lt;/i&gt; &lt; 0.001) in Round 2; both values indicate weak-to-moderate agreement, and because the two rounds evaluated non-identical item sets these coefficients are reported as within-round desc","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1880574"},"PeriodicalIF":5.2,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13612560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066285","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 and evaluation of a voice dataset for voice therapy exercises: quantitative audio analysis and user feedback from a cross-sectional study.","authors":"Sabrina H Schröder, Rica Schulze, Merle Schlender, Torge Jensen, Frank Schulze, Navid Tabriz, Andrea Henze, Michael Buschermöhle, Sebastian Fudickar, Dirk Weyhe, Verena Uslar","doi":"10.3389/fdgth.2026.1835534","DOIUrl":"10.3389/fdgth.2026.1835534","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to develop and validate a high-quality voice dataset for dysphonia therapy using a standardized, video-guided recording protocol comprising a range of therapeutic voice exercises. The dataset is designed to support the development of digital health applications that enable home-based voice training with real-time biofeedback, thereby addressing current challenges in speech-language pathology, including limited access to therapy and the need for scalable, patient-centered interventions.</p><p><strong>Methods: </strong>A voice dataset was collected from adult participants performing voice therapy exercises in German, recorded via smartphone and in-ear microphones under standardized conditions. In addition to acoustic data collection, participants evaluated the recording procedure and video-guided exercise instructions. Acoustic analyses of pitch (Hz), intensity (dB), and Cepstral Peak Prominence (CPP) were conducted using Praat, with data grouped by age and sex. Correlation analyses were performed to assess the consistency of recordings across participants and exercises.</p><p><strong>Results: </strong>The resulting dataset comprises recordings from 175 adults, totaling approximately 6.6 GB of audio data, with a subset made available as open access. Participants reported high overall acceptance of the recording procedure and instructional videos, while also identifying areas for improvement, particularly regarding pacing and clarity. Acoustic and correlation analyses demonstrated exercise-specific variations in CPP, pitch and intensity, as well as systematic differences across age and gender groups, indicating both variability and structured consistency within the dataset.</p><p><strong>Conclusion: </strong>The dataset represents a novel resource for digital voice therapy, particularly due to its focus on structured therapeutic exercises in German. It provides a robust foundation for the development and evaluation of digital health applications aimed at enhancing self-managed voice training and supporting therapy processes beyond clinical settings.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1835534"},"PeriodicalIF":5.2,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13614008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066263","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":"Maternal mHealth usability strengthened by trust, governance, and navigation.","authors":"Ehiremen Adesua Azugbene","doi":"10.3389/fdgth.2026.1825544","DOIUrl":"10.3389/fdgth.2026.1825544","url":null,"abstract":"<p><p>Maternal mobile health (mHealth) technologies have expanded rapidly, offering scalable tools to support education, reminders, symptom monitoring, and patient engagement across the perinatal continuum. Despite demonstrated improvements in usability and intermediate behavioral outcomes, persistent disparities in maternal morbidity and mortality highlight the limits of engagement-driven digital innovation. This Perspective advances the viewpoint that maternal mHealth effectiveness depends not only on user-centered design but on alignment with health system structures, governance practices, trust dynamics, healthcare navigation, and navigable care pathways. Drawing on behavioral science, implementation research, digital health governance scholarship, and maternal health equity literature, this paper examines (1) the promise and limits of maternal mHealth, (2) the gap between engagement and clinically meaningful impact, (3) trust as foundational infrastructure, (4) governance and transparency as structural determinants of legitimacy and sustainability, and (5) navigation as the operational bridge between digital activation and real-world maternal healthcare utilization. We propose reframing maternal mHealth as connective infrastructure rather than a feature-driven product model. Within this framework, healthcare navigation is conceptualized as a multidimensional construct comprising behavioral, technological, organizational, and relational dimensions that enable women to move effectively through fragmented healthcare systems. In this framework, usability and engagement remain essential but must be integrated with transparent governance, participatory processes, culturally responsive navigation, and system alignment to translate digital prompts into timely care utilization and equitable maternal health outcomes. Designing for both systems and users positions maternal mHealth as a structural intervention capable of advancing perinatal engagement and reducing disparities.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1825544"},"PeriodicalIF":5.2,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13612515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066526","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":"Clinical warrant in medical LLM evaluation.","authors":"Murat Sariyar","doi":"10.3389/fdgth.2026.1918724","DOIUrl":"10.3389/fdgth.2026.1918724","url":null,"abstract":"<p><p>Medical large language models are evaluated through factuality, physician preference, source support, retrieval quality, safety, and clinical utility. These measures describe content and performance while leaving unresolved whether a generated claim meets the requirements for a specified clinical use. This article proposes <i>clinical warrant</i> as a use-specific judgment for a particular output-use pair. Clinical use includes reliance within a patient-related encounter, task, or care pathway, including education that can shape understanding or later action. The proposed use and the standards used to judge it are documented separately. For action-guiding outputs, clinical appropriateness is a gateway. Five further conditions examine role, case information, evidence, preserved limits, and authority/accountability. The framework asks whether these requirements are met at the point of proposed reliance, records failed or unresolved conditions and the resulting disposition, and tests whether a relevant change stops or redirects use. Fifteen constructed examples across five domains distinguish established, not established, and indeterminate warrant.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1918724"},"PeriodicalIF":5.2,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13601723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042353","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":"AI-generated medical realism and hantavirus misinformation: a new public health threat?","authors":"Marta Colaneri, Alice Baratelli, Sara Laura Ferrari, Juliana Viazzo, Andrea Gori, Simone Di Stefano, Alberto Rizzo, Fabio Borgonovo, Giovanni Scaglione, Stefano Odelli","doi":"10.3389/fdgth.2026.1887820","DOIUrl":"10.3389/fdgth.2026.1887820","url":null,"abstract":"<p><p>The widespread availability of generative artificial intelligence (AI) has substantially reshaped the health-information environment compared with the COVID-19 pandemic. Contemporary AI tools can rapidly generate highly realistic medical-looking content from text, including laboratory reports, radiological images, histological slides, and institutional clinical documentation. During infectious disease outbreaks, such simulated medical content may exploit the visual authority traditionally associated with healthcare institutions and influence public risk perception. During media coverage of the May 2026 Andes virus (ANDV) alert in Italy, news outlets published realistic AI-generated medical imagery depicting hantavirus diagnostic reports, hospital-associated laboratory materials, and outbreak-response scenes linked to real healthcare institutions. One image, in particular, reproduced the visual identity of a real laboratory report produced at \"Luigi Sacco\" University Hospital despite displaying multiple inconsistencies. In some additional examples, AI generation was not clearly disclosed despite the use of recognizable institutional names and logos. This phenomenon has the potential to extend beyond isolated misinformation events. Emerging evidence indicates that experts may struggle to reliably distinguish AI-generated biomedical and radiological images from authentic materials, raising concern that future infectious disease outbreaks may occur within an information ecosystem increasingly shaped by visually persuasive synthetic media. Such content may amplify public anxiety, distort perceptions of authority, and erode trust in institutions. Synthetic medical realism should therefore be considered a potential emerging category of public-health risk during infectious disease emergencies that requires coordinated action from institutions and regulators developing verification systems, while news media, platforms, and publishers implement AI-disclosure standards and guidelines for synthetic biomedical content.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1887820"},"PeriodicalIF":5.2,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13602951/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042414","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":"Correction: Technological innovation in healthcare: challenges, opportunities and impact on sustainability.","authors":"María Vallet-Regí, Manuel Doblaré, José A Garrido, Almudena Fuster-Matanzo, Ángel Alberich-Bayarri, María Elena Hernando, Cecilia E García Cena, Marie Destarac Eguizabal, Emilio Bouza","doi":"10.3389/fdgth.2026.1936077","DOIUrl":"https://doi.org/10.3389/fdgth.2026.1936077","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fdgth.2026.1720615.].</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1936077"},"PeriodicalIF":5.2,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13598496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042441","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":"Quality and reliability of myelodysplastic syndromes-related health information on Chinese short-video platforms: a multi-platform cross-sectional analysis.","authors":"Ruijun Yang, Wanjia Chen, Caixian Xu, Haotian Yan, Wenjie Dai, Yihui Lin, Zhimin Zhai, Wanqiu Zhang, Qianshan Tao","doi":"10.3389/fdgth.2026.1956383","DOIUrl":"10.3389/fdgth.2026.1956383","url":null,"abstract":"<p><strong>Background: </strong>Myelodysplastic syndromes (MDS) are common hematologic malignancies among older adults. With the rapid growth of short-video platforms, patients increasingly seek MDS-related health information online; however, the quality of MDS-related health information available on short-video platforms has not been systematically evaluated.</p><p><strong>Objective: </strong>This study assessed the quality and reliability of MDS-related short videos across three major Chinese platforms and examined the relationship between content quality and user engagement.</p><p><strong>Methods: </strong>Between June 25 and June 30, 2026, a total of 210 MDS-related videos were systematically collected from TikTok (Chinese version: Douyin), Bilibili, and Xiaohongshu (70 videos per platform). Video metadata were extracted, and content quality was independently evaluated using three validated instruments: the modified DISCERN (mDISCERN), the Journal of the American Medical Association (JAMA) benchmark criteria, and the Global Quality Scale (GQS). An overall quality score (SUM) was calculated by combining the three assessment tools. Spearman correlation analysis was performed to examine the associations between user engagement metrics and content quality.</p><p><strong>Results: </strong>Bilibili videos demonstrated the highest overall educational quality (mean SUM score: 9.79 ± 2.35), whereas TikTok generated the greatest user engagement (median number of likes: 81.00). Videos produced by professional creators achieved significantly higher scores across all quality assessment instruments than those produced by non-professional creators (mean SUM score: 8.81 vs. 5.03, <i>p</i> < 0.001). No significant associations were observed between any user engagement metric and content quality. Video duration was the only variable positively correlated with content quality (<i>r</i> = 0.332, <i>p</i> < 0.01).</p><p><strong>Conclusions: </strong>User engagement was disconnected from information quality in MDS-related short videos. Professional creators provided greater educational value without consistently receiving commensurate engagement. Digital health communication should prioritize expert-led content, transparent source attribution, and quality-informed dissemination.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1956383"},"PeriodicalIF":5.2,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042436","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}