Frontiers in digital health最新文献

{"title":"Decoding digital transformation in health professions education: a comparative analysis of knowledge, attitudes, and readiness among faculty and students at KSAU-HS.","authors":"Amal I Khalil, Lamyaa Y ALyaba, Amnah M Jambi","doi":"10.3389/fdgth.2026.1881377","DOIUrl":"10.3389/fdgth.2026.1881377","url":null,"abstract":"<p><strong>Introduction: </strong>Digital transformation is altering educational processes in health professions and needs instructors and students to acquire competencies for its implementation. At the same time, comparative information about readiness for digital transformation for educators and students in Saudi Arabian universities is scarce and the reasons for such readiness are not fully known. This study reviewed the knowledge, attitude and readiness of instructors and learners at King Saud bin Abdulaziz University for Health Sciences.</p><p><strong>Methods: </strong>The study used a cross-sectional comparative design. Data was collected from the respondents with the help of a specially designed questionnaire, based on the Technology Acceptance Model. The questionnaire measured digital competency, perceived usefulness and ease of use and facilitating conditions.</p><p><strong>Results: </strong>Respondents displayed a slightly more developed level of digital competence, perceived usefulness, perceived ease of use, and readiness for digital transformation. As for students, motivation and confidence in one's ability to work with digital technologies turned out to be the best predictors. Structural equation modeling has validated that usefulness and ease of use along with facilitating conditions represent significant factors for predicting the intention to use digital technologies. Hence, psychology and organizational aspects play a bigger role in being ready for the digital transformation than demographic variables.</p><p><strong>Discussion: </strong>The readiness for digital transformation in health professions education relates to not only the technical aspect but also to individual motivation, confidence, and institutional backing. Therefore, universities should develop specific strategies to motivate and encourage students to learn, along with training faculty, giving sufficient resources, and offering suitable organizational conditions.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1881377"},"PeriodicalIF":5.2,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13619956/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149083060","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":"From data to action: the FRAME conceptual framework for translating consumer wearable feedback into behavior change support.","authors":"Sundus Malaikah","doi":"10.3389/fdgth.2026.1871670","DOIUrl":"10.3389/fdgth.2026.1871670","url":null,"abstract":"<p><strong>Background: </strong>Consumer wearables are now widely used across health promotion, wellness, and recreational fitness. Although wearable-supported interventions can improve physical activity and related outcomes, effects are typically modest, heterogeneous, and more consistent when devices are embedded within broader behavior change support rather than used alone. In applied settings, however, wearable use often remains focused on metric display and passive monitoring, leaving practitioners without a clear framework for translating data into behaviorally meaningful support.</p><p><strong>Objective: </strong>This paper proposes FRAME, a theory-informed conceptual framework intended to help practitioners interpret consumer wearable outputs as behaviorally relevant signals rather than as self-sufficient indicators of progress or failure.</p><p><strong>Methods: </strong>The framework was developed through a targeted narrative synthesis of literature on wearable-supported interventions, self-monitoring, feedback design, engagement, adaptive support, and applied behavior change theory. The purpose of the synthesis was not to estimate pooled effects, but to identify recurring interpretive and implementation problems that arise when wearable data are used in real-world behavior support encounters.</p><p><strong>Framework overview: </strong>FRAME comprises five linked steps: Filter the signal, Read the behavior pattern, Align a behavior change target, Message and micro-plan, and Evaluate and iterate. Across these steps, wearable data are treated as inputs into interpretation, hypothesis generation, collaborative planning, and iterative review. The framework is grounded primarily in self-regulation, the Capability-Opportunity-Motivation-Behavior (COM-B) model, self-determination theory, social cognitive theory, habit formation, and relapse prevention.</p><p><strong>Conclusion: </strong>For practitioners, the framework offers a structured way to judge whether a wearable signal is usable, what behavior pattern it may reflect, which target to prioritize, and how to discuss the data in an autonomy-supportive way. For wearable users, this is intended to yield more collaborative and sustainable behavior change support.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1871670"},"PeriodicalIF":5.2,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13619601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149083070","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":"Growing public interest and regional disparities of cardiorenal syndrome in the United States.","authors":"Reid P Schlesinger, Kianoush B Kashani, Charat Thongprayoon, Wisit Cheungpasitporn, Jing Miao","doi":"10.3389/fdgth.2026.1803510","DOIUrl":"10.3389/fdgth.2026.1803510","url":null,"abstract":"<p><strong>Background: </strong>Public awareness and regional engagement with cardiorenal syndrome (CRS) remain poorly understood. We examined temporal trends and geographic patterns of U.S. public interest in CRS and their relationship with disease burden.</p><p><strong>Methods: </strong>Google Trends™ data (2004-2025) annual relative search interest (RSI) for CRS. CRS-related mortality was derived from CDC WONDER (2007-2020) using hypertensive heart and chronic kidney disease deaths. Temporal trends were assessed via augmented Dickey-Fuller tests and linear regression. Associations between RSI and mortality were evaluated using Pearson correlation, AR(1) modeling, first-difference, and cumulative change analysis. State-level correlations were examined with and without adjustment for income, education, and insurance coverage.</p><p><strong>Results: </strong>Both RSI and mortality exhibited strong non-stationary upward trend (<i>p</i> = 0.98 and 0.42). Raw data showed a strong correlation analysis (r = 0.79, <i>p</i> = 0.001); but first-differenced year-over-year changes were not significant (r = -0.36, <i>p</i> = 0.23), consistent with AR(1) modeling (<i>β</i>=1.58, <i>p</i> = 0.11). Cumulative changes remained strongly correlated (r = 0.80, <i>p</i> = 0.001), indicating parallel long-term trajectories. Findings were consistent with varying ICD-10 code definitions for CRS-related mortality. Geographically, RSI was highest in West Virginia and lowest in Texas and Georgia. State-level RSI showed no correlation with age-adjusted mortality, both unadjusted (r = -0.09, <i>p</i> = 0.52) and after adjustment for median income, educational, and insurance coverage (<i>β</i>=0.03, <i>p</i> = 0.75).</p><p><strong>Conclusion: </strong>Public interest in CRS has grown in parallel with mortality, reflecting its increasing epidemiological relevance. However, the lack of geographic correlation between search interest and mortality-even after socioeconomic adjustment-suggests awareness gaps and highlights opportunities for targeted education and resource allocation.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1803510"},"PeriodicalIF":5.2,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13619914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149083116","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":"Lightweight structure-aware explanations for rule-based models: application to radiotherapy risk communication in breast cancer.","authors":"Alessandro Umbrico, Guido Bologna, Luca Coraci, Francesca Fracasso, Silvia Gola, Gabriella Cortellessa","doi":"10.3389/fdgth.2026.1925421","DOIUrl":"10.3389/fdgth.2026.1925421","url":null,"abstract":"<p><strong>Objective: </strong>Rule-based models are often considered inherently interpretable because their explicit logical structure allows users to trace predictions back to human-readable rules. However, in complex domains such as healthcare, multiple rules involving numerous attributes may be activated simultaneously, making it difficult for end users to identify the most influential factors behind a prediction. This can limit practical transparency, particularly in clinical shared decision-making, where clear and trustworthy explanations are essential. An example is communicating the risk of side effects following radiotherapy for breast cancer, where understanding the determinants of estimated risk may support informed treatment choices.</p><p><strong>Method: </strong>We propose a lightweight, structure-aware attribution method for rule-based models inspired by information retrieval techniques. The approach represents the activated rules associated with a prediction as a document-like structure and applies a TF-IDF-inspired weighting scheme to estimate the relevance of individual input attributes. Unlike perturbation-based methods such as SHAP and LIME, the proposed approach operates directly on the internal model structure, without requiring input perturbations, surrogate models, repeated evaluations, or access to training data. This preserves the explanatory signal encoded in the rules while reducing computational complexity and execution time. The method is evaluated through a technical comparison with SHAP and LIME and a user-centered study involving potential end users.</p><p><strong>Results and conclusions: </strong>Experimental results show moderate agreement between the TF-IDF-inspired ranking and established attribution methods, identifying attributes that are behaviorally informative with respect to the implemented predictor. Faithfulness analysis shows that direct rule frequency achieves higher perturbation faithfulness, highlighting a trade-off introduced by the proposed weighting scheme. Complementarily, the user study indicates that the complete explanation pipeline, combining relevance weighting, semantic aggregation, and user-oriented presentation, is perceived as more interpretable and useful than raw rule-based explanations. These findings suggest that information-retrieval-inspired weighting can provide a lightweight mechanism for organizing rule evidence, while semantic abstraction and user-oriented presentation can facilitate its communication to end users.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1925421"},"PeriodicalIF":5.2,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13617116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076781","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":"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":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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}