Frontiers in digital health最新文献

{"title":"Advancing personalized digital therapeutics: integrating music therapy, brainwave entrainment methods, and AI-driven biofeedback.","authors":"Dian Jiao","doi":"10.3389/fdgth.2025.1552396","DOIUrl":"10.3389/fdgth.2025.1552396","url":null,"abstract":"<p><p>Mental health disorders and cognitive decline are pressing global concerns, increasing the demand for non-pharmacological interventions targeting emotional dysregulation, memory deficits, and neural dysfunction. This review systematically examines three promising methodologies-music therapy, brainwave entrainment (binaural beats, isochronic tones, multisensory stimulation), and their integration into a unified therapeutic paradigm. Emerging evidence indicates that music therapy modulates affect, reduces stress, and enhances cognition by engaging limbic, prefrontal, and reward circuits. Brainwave entrainment, particularly within the gamma frequency range (30-100 Hz), facilitates neural oscillatory patterns linked to relaxation, concentration, and memory, with 40 Hz showing promise for cognitive enhancement, albeit with individual variability. Synchronized multisensory stimulation, combining auditory and visual inputs at gamma frequencies, has demonstrated potential in enhancing memory and supporting neural integrity, particularly in Alzheimer's disease. However, challenges such as patient response variability, lack of standardization, and scalability hinder widespread implementation. Recent research suggests that a synergistic application of these modalities may optimize therapeutic outcomes by leveraging complementary mechanisms. To actualize this, AI-driven biofeedback, enabling real-time physiological assessment and individualized adjustments-such as tailoring musical complexity, entrainment frequencies, and multisensory components-emerges as a promising solution. This adaptive model enhances treatment accessibility and consistency while maximizing long-term efficacy. Although in early stages, preliminary evidence highlights its transformative potential in reshaping non-pharmacological therapeutic strategies. Advancing this field requires interdisciplinary research, rigorous evaluation, and ethical data stewardship to develop innovative, patient-centered solutions for mental health and cognitive rehabilitation.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1552396"},"PeriodicalIF":3.2,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607355","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":"Unlocking health insights: exploring intention to adopt district health information systems in Bahir Dar City, northwest Ethiopia.","authors":"Habtamu Alganeh Guadie, Amarech Kindie, Hunegnaw Almaw Derseh, Desta Debalkie Atnafu","doi":"10.3389/fdgth.2025.1449510","DOIUrl":"10.3389/fdgth.2025.1449510","url":null,"abstract":"<p><strong>Background: </strong>District Health Information System version 2 (DHIS2) is an open-source platform designed for data collection, processing, analysis, and visualization within healthcare systems. However, there is limited empirical evidence regarding health professionals' intentions to use district health information systems. Understanding the factors influencing health workers' intention to utilize DHIS2 is crucial for ensuring successful implementation and sustained usage. This study aimed to assess the intention to use DHIS2 and identify associated factors among health professionals in health centers of Bahir Dar Metropolitan City, Ethiopia, in 2022.</p><p><strong>Methods: </strong>An institutional-based cross-sectional study was conducted involving 368 randomly selected health professionals from health centers in Bahir Dar City, Ethiopia, between 24 May and 24 June 2022. Data were collected using a structured, self-administered questionnaire. Completed questionnaires were entered and coded in EpiData version 4.6 and exported to SPSS version 25 for cleaning and statistical analysis. Descriptive statistics and bivariate and multivariable logistic regression analyses were performed. Model fitness was assessed using the Hosmer-Lemeshow goodness-of-fit test, with statistical significance set at a <i>p</i>-value < 0.05 and a 95% confidence interval.</p><p><strong>Results: </strong>A total of 342 participants completed the study, resulting in a response rate of 92.9%. The sample included 176 (51.5%) women, of whom 147 (43%) were nurses. Nearly two-thirds (65.2%) of health professionals expressed an intention to use the DHIS2 system. The intention to use DHIS2 was significantly associated with factors including attitude, computer skills, perceived utility, and perceived ease of use.</p><p><strong>Conclusion: </strong>The findings indicate that attitude, perceived utility, perceived ease of use, and computer skills significantly influence the intention to utilize DHIS2. Therefore, it is imperative to implement targeted interventions before system rollout, including practice-based training, fostering positive attitudes, and enhancing knowledge of the system's usability and functionality to improve the adoption of the district health information system.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1449510"},"PeriodicalIF":3.2,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11895800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607408","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":"Consent mechanisms and default effects in health information exchange in Japan.","authors":"Atsushi Ito, Fumihiko Nakamura","doi":"10.3389/fdgth.2025.1498072","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1498072","url":null,"abstract":"<p><strong>Background: </strong>Health information exchange (HIE) is an information system that efficiently shares patient information across medical institutions. However, traditional consent methods, represented by opt-in and opt-out, face a trade-off between efficiency and ethical, making it difficult to fundamentally improve consent rates. To address this issue, we focused on default settings and proposed an innovative approach called the \"two-step consent model,\" which leverages the advantages of existing models using utility theory. We evaluated the acceptability of this method.</p><p><strong>Methods: </strong>An online survey was conducted with 2,000 participants registered with Japan's largest internet survey company. We compared and analyzed the consent rates of the opt-in, opt-out, and two-step consent models.</p><p><strong>Results: </strong>The opt-in model had a 29.5% consent rate, maximizing patient autonomy but increasing the burden and reducing efficiency. The opt-out model had a 95.0% consent rate but raised concerns among half of the respondents. The two-step consent model had a 68.5% consent rate, demonstrating its cost-effectiveness compared with traditional models.</p><p><strong>Discussion: </strong>The two-step consent model, involving implicit and explicit consent when needed, ensures efficient consent acquisition while respecting patient autonomy. It is a cost-effective policy option that can overcome the ethical issues associated with the opt-out model. Introducing methods that leverage both opt-in and opt-out advantages is expected to address HIE stagnation.</p><p><strong>Conclusion: </strong>The two-step consent model is expected to improve consent rates by balancing the efficiency and quality of consent acquisition. To achieve this, patient education is crucial for raising awareness and understanding of HIE and its consent methods.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1498072"},"PeriodicalIF":3.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11908376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652444","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 active participation in robot-aided rehabilitation via machine learning and impedance control.","authors":"Christian Tamantini, Kevin Patrice Langlois, Joris de Winter, Parham Haji Ali Mohamadi, David Beckwée, Eva Swinnen, Tom Verstraten, Bram Vanderborght, Loredana Zollo","doi":"10.3389/fdgth.2025.1559796","DOIUrl":"10.3389/fdgth.2025.1559796","url":null,"abstract":"<p><strong>Introduction: </strong>Active patient participation is crucial for effective robot-assisted rehabilitation. Quantifying the user's Active Level of Participation (ALP) during therapy and developing human-robot interaction strategies that promote engagement can improve rehabilitation outcomes. However, existing methods for estimating participation are often unimodal and do not provide continuous participation assessment.</p><p><strong>Methods: </strong>This study proposes a novel approach for estimating ALP during upper-limb robot-aided rehabilitation by leveraging machine learning within a multimodal framework. The system integrates pressure sensing at the human-robot interface and muscle activity monitoring to provide a more comprehensive assessment of user participation. The estimated ALP is used to dynamically adapt task execution time, enabling an adaptive ALP-driven impedance control strategy. The proposed approach was tested in a laboratory setting using a collaborative robot equipped with the sensorized interface. A comparative analysis was conducted against a conventional impedance controller, commonly used in robot-aided rehabilitation scenarios.</p><p><strong>Results: </strong>The results demonstrated that participants using the ALP-driven impedance control exhibited significantly higher positive mechanical work and greater muscle activation compared to the control group. Additionally, subjective feedback indicated increased engagement and confidence when interacting with the adaptive system.</p><p><strong>Discussion: </strong>Closing the robot's control loop by adapting to ALP effectively enhanced human-robot interaction and motivated participants to engage more actively in their therapy. These findings suggest that ALP-driven control strategies may improve user involvement in robot-assisted rehabilitation, warranting further investigation in clinically relevant settings.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1559796"},"PeriodicalIF":3.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11885312/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588422","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":"Prism adaptation combined with serious games for improving visual-constructive abilities in stroke patients: randomized clinical trial.","authors":"Massimiliano Oliveri, Sergio Bagnato, Silvia Rizzo, Emilia Imbornone, Patrizia Turriziani","doi":"10.3389/fdgth.2025.1425410","DOIUrl":"10.3389/fdgth.2025.1425410","url":null,"abstract":"<p><strong>Introduction: </strong>Visuomotor adaptation to a displacement of the visual field induced by prismatic lenses can help rehabilitate cognitive deficits when combined with digital cognitive training. The aim of this study was to evaluate the effectiveness of this approach in rehabilitating visual constructive deficits in stroke patients, assess the generalization of improvements to daily living skills, identify which serious games best predicted improvements.</p><p><strong>Methods: </strong>Thirty stroke patients were randomly assigned to either a control group, receiving standard rehabilitation, or an experimental group, receiving a therapy combining prism adaptation with cognitive training through serious games over ten consecutive sessions. Patients were administered a neuropsychological test battery at baseline (T0) and after 10 days (T1). Visual constructive abilities were evaluated using Freehand Copy of Drawings and Copy of Drawings with Landmarks tests. Spatial attention was evaluated using Albert's Line Cancellation and Line Bisection tests. Functional abilities were evaluated with the Barthel Index.</p><p><strong>Results: </strong>Test scores of the Freehand Copy of Drawings improved from T0 to T1 in both the experimental (6.89 ± 2.7 vs. 7.83 ± 2.9; <i>p</i> = 0.01) and the control group (5.84 ± 2.1 vs. 7.51 ± 2.2; <i>p</i> = 0.01). The improvement was comparable between the two groups (<i>p</i> = 0.38). Test scores of the Copy of Drawings with Landmarks improved from T0 to T1 in the experimental (42.94 ± 19.6 vs. 50.2 ± 18.1; <i>p</i> = 0.007), but not in the control group (39.9 ± 19.6 vs. 42.7 ± 20.9; <i>p</i> = 0.41). The improvement was comparable between the two groups (<i>p</i> = 0.28). In the experimental group, Barthel Index scores at T1 correlated with both Free Hand Copy of Drawings scores (<i>R</i> = 0.651; <i>p</i> = 0.009) and Copy of Drawings with Landmarks scores (<i>R</i> = 0.582; <i>p</i> = 0.02). No correlations were found in the Control Group. Serious games targeting attention and motor planning were predictive of improvements in visual construction.</p><p><strong>Conclusion: </strong>prismatic lenses combined with digital cognitive training improve visual construction and functional abilities in stroke patients, providing a novel method to promote stroke rehabilitation.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1425410"},"PeriodicalIF":3.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11885297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588415","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":"Using machine learning models for cuffless blood pressure estimation with ballistocardiogram and impedance plethysmogram.","authors":"Shing-Hong Liu, Yao Sun, Bo-Yan Wu, Wenxi Chen, Xin Zhu","doi":"10.3389/fdgth.2025.1511667","DOIUrl":"10.3389/fdgth.2025.1511667","url":null,"abstract":"<p><strong>Introduction: </strong>Blood pressure (BP) serves as a crucial parameter in the management of three prevalent chronic diseases, hypertension, cardiovascular diseases, and cerebrovascular diseases. However, the conventional sphygmomanometer, utilizing a cuff, is unsuitable for the approach of mobile health (mHealth).</p><p><strong>Methods: </strong>Cuffless blood pressure measurement, which eliminates the need for a cuff, is considered a promising avenue. This method is based on the relationship between pulse arrival time (PAT) parameters and BP. In this study, pulse transit time (PTT) was derived from ballistocardiograms (BCG) and impedance plethysmograms (IPG) obtained from a weight-fat scale. This study aims to address two challenges using deep learning and machine learning technologies: first, identifying BCG and IPG signals with good quality, and then extracting PTT parameters from them to estimate BP. A stacked model comprising a one-dimensional convolutional neural network (1D CNN) and gated recurrent unit (GRU) was proposed to classify the quality of BCG and IPG signals. Seven parameters, including calibration-based and calibration-free PTT parameters and heart rate (HR), were examined to estimate BP using random forest (RF) and XGBoost models. Seventeen healthy subjects participated in the study, with their BP elevated through exercise. A digital sphygmomanometer was employed to measure BP as reference values. Our methodology was validated using data collected from our custom-made device.</p><p><strong>Results: </strong>The results demonstrated a signal quality classification accuracy of 0.989. Furthermore, in the five-fold cross-validation, Pearson correlation coefficients of 0.953 ± 0.007 and 0.935 ± 0.007 were achieved for systolic BP (SBP) and diastolic BP (DBP) estimations, respectively. The mean absolute differences (MADs) of XGBoost model were calculated as 3.54 ± 0.34 and 2.57 ± 0.17 mmHg for SBP and DBP, respectively.</p><p><strong>Discussion: </strong>The proposed method significantly improved the accuracy of cuffless BP measurement, indicating its potential integration into weight-fat scales as an unconstrained device for effective utilization in mHealth applications.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1511667"},"PeriodicalIF":3.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11885288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588488","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":"Virtual reality for training emergency medicine residents in emergency scenarios: usefulness of a tutorial to enhance the simulation experience.","authors":"A Vittadello, S Savino, S Bressan, M Costa, A Boscolo, N Sella, T Pettenuzzo, F Zarantonello, A De Cassai, T Chang, P Navalesi, G Mormando","doi":"10.3389/fdgth.2025.1466866","DOIUrl":"10.3389/fdgth.2025.1466866","url":null,"abstract":"<p><strong>Introduction: </strong>Critical events in healthcare require a rapid and coordinated approach: simulation has been demonstrated a valid technique for training in emergency. Virtual Reality (VR) is an innovative technology that has revolutionized simulation training and healthcare professional development. A key phase of a simulation session with manikin consists in a familiarization with setting and equipment. The primary objective of this study is to investigate whether familiarization with a VR tutorial can change the perception of cases.</p><p><strong>Methods: </strong>Emergency medicine residents were randomly assigned to the Intervention group (n = 21) who undergone familiarization tutorial prior to the clinical scenario to a Control group (<i>n</i> = 21) where no familiarization tutorial was provided before the clinical scenario.</p><p><strong>Results: </strong>No significant differences were found between the two groups regarding perceived ease of use, but the Intervention group found VR familiarization useful and the Control group found it necessary to implement a VR tutorial. VR training was generally perceived by learners as a useful technology for training as confirmed by the literature.</p><p><strong>Discussion: </strong>Familiarization seems to be an important phase of simulation-based training for trainees, even when running a VR-based simulation for an emergency scenario; it should be incorporated into the clinical VR sessions for simulation in healthcare settings.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1466866"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558433","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":"Acceptance of a mental health app (JoyPop^TM) for postsecondary students: a prospective evaluation using the UTAUT2.","authors":"Ishaq Malik, Aislin R Mushquash","doi":"10.3389/fdgth.2025.1503428","DOIUrl":"10.3389/fdgth.2025.1503428","url":null,"abstract":"<p><strong>Introduction: </strong>Mental health (MH) smartphone applications (MH apps) can support the increasing MH needs of postsecondary students and mitigate barriers to accessing support. Evaluating MH app acceptance using technology acceptance models is recommended to improve student engagement with MH apps. The JoyPop^TM app was designed to improve youth resilience and emotion regulation. The JoyPop^TM app is associated with improved student MH, but its acceptance has yet to be evaluated quantitatively. The present study used the Unified Theory of Acceptance and Use of Technology (UTAUT2) to evaluate and examine constructs and moderators influencing the acceptance (i.e., behavioural intention) and use of the JoyPop^TM app.</p><p><strong>Method: </strong>Participants were 183 postsecondary students attending a Canadian University who used the app for one week and completed measures before and after using the app. Relationships posited by the UTAUT2 were tested using partial least squares structural equation modelling (PLS-SEM).</p><p><strong>Results: </strong>Most participants accepted the JoyPop^TM app. The UTAUT2 model explained substantial variance in behavioural intention and app use. Performance expectancy, hedonic motivation, and facilitating conditions predicted behavioural intention, and behavioural intention and facilitating conditions predicted app use. Age moderated the association between facilitating conditions and behavioural intention. Experience moderated the relationship between performance expectancy, hedonic motivation, and social influence on behavioural intention.</p><p><strong>Discussion: </strong>Results provide insight into factors influencing the acceptance of the JoyPop^TM app and its ability to engage students. Results also provide valuable insights for evaluating and optimally designing MH apps.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1503428"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560349","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":"Assessing accuracy of BiliPredics algorithm in predicting individual bilirubin progression in neonates-results from a prospective multi-center study.","authors":"Britta Steffens, Gilbert Koch, Corinna Engel, Axel R Franz, Marc Pfister, Sven Wellmann","doi":"10.3389/fdgth.2025.1497165","DOIUrl":"10.3389/fdgth.2025.1497165","url":null,"abstract":"<p><strong>Background: </strong>Neonatal jaundice affects more than half of neonates. As bilirubin values usually peak few days after hospital discharge, jaundice remains a leading cause of rehospitalization. The recently developed BiliPredics algorithm, integrated in the first CE-approved bilirubin prediction tool, predicts individual bilirubin progression for up to 60 h into the future. Goal of the prospective study was to assess accuracy of this algorithm in predicting individual bilirubin prior to hospital discharge in neonates.</p><p><strong>Methods: </strong>A prospective multi-center study was conducted in 2021 at the University Children's Hospitals in Tübingen and Regensburg, Germany. Various scenarios differing in type and number of bilirubin measurements and in prediction horizon were tested. Primary objective was prediction accuracy of the BiliPredics algorithm based on total serum bilirubin (TSB) measurements or based on transcutaneous bilirubin (TcB) measurements alone. Secondary objective was prediction accuracy based on combinations of TSB and TcB measurements. For assessment of accuracy, two validation metrics, absolute prediction error <math><mo>(</mo> <mrow><mi>a</mi> <mi>P</mi> <mi>E</mi></mrow> <mo>)</mo></math> and relative prediction error <math><mo>(</mo> <mrow><mi>r</mi> <mi>P</mi> <mi>E</mi></mrow> <mo>)</mo></math> , and two clinical acceptance conditions, margin of error of the 95%-confidence interval (95%-CI) and percentage of clinically relevant mis-predictions defined as <math><mi>a</mi> <mi>P</mi> <mi>E</mi> <mo>></mo> <mn>85</mn> <mspace></mspace> <mspace></mspace> <mi>μ</mi> <mrow><mi>mol</mi> <mo>/</mo> <mi>L</mi></mrow> </math> , were investigated.</p><p><strong>Results: </strong>Out of 455 enrolled neonates, 276 neonates met bilirubin inclusion criteria and were included in the analyses. Irrespective from tested prediction horizons, median <math><mi>r</mi> <mi>P</mi> <mi>E</mi></math> was small (8.5% to 9.5%) utilizing TSB measurements for up to 30 and 60 h and slightly higher (13.8%) utilizing TcB measurements for up to 48 h. The same applied for median <math><mi>a</mi> <mi>P</mi> <mi>E</mi></math> . Both clinical acceptance conditions were fulfilled across tested scenarios. Results for combined TSB-TcB scenarios up to a prediction horizon of 48 h without adjustment for type of measurement were comparable to TSB and TcB scenarios fulfilling both clinical acceptance conditions.</p><p><strong>Conclusion: </strong>Results from this prospective study in neonates confirm that the BiliPredics algorithm accurately predicts bilirubin progression up to 60 h with TSB measurements and up to 48 h with TcB or combined TSB-TcB measurements. As such, prediction tools utilizing this algorithm are expected to facilitate and safely optimize jaundice risk assessment at hospital discharge with the potential to reduce jaundice-related rehospitalizations.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1497165"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11878101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560352","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":"Adaptation of a digital health intervention for rural adults: application of the Framework for Reporting Adaptations and Modifications-Enhanced.","authors":"Maura M Kepper, Callie Walsh-Bailey, Loni Parrish, Ainsley Mackenzie, Lisa M Klesges, Peg Allen, Kia L Davis, Randi Foraker, Ross C Brownson","doi":"10.3389/fdgth.2025.1493814","DOIUrl":"10.3389/fdgth.2025.1493814","url":null,"abstract":"<p><strong>Introduction: </strong>Adaptation is a key aspect of implementation science; interventions frequently need adaptation to better fit their delivery contexts and intended users and recipients. As digital health interventions are rapidly developed and expanded, it is important to understand how such interventions are modified. This paper details the process of engaging end-users in adapting the PREVENT digital health intervention for rural adults and systematically reporting adaptations using the Framework for Reporting Adaptations and Modifications-Enhanced (FRAME). The secondary objective was to tailor FRAME for digital health interventions and to document potential implications for equity.</p><p><strong>Methods: </strong>PREVENT's adaptations were informed by two pilot feasibility trials and a planning grant which included advisory boards, direct clinic observations, and qualitative interviews with patients, caregivers, and healthcare team members. Adaptations were catalogued in an Excel tracker, including a brief description of the change. Pilot coding was conducted on a subset of adaptations to revise the FRAME codebook and generate consensus. We used a directed content analysis approach and conducted a secondary data analysis to apply the revised FRAME to all adaptations made to PREVENT (<i>n</i> = 20).</p><p><strong>Results: </strong>All but one adaptation was planned, most were reactive (versus proactive), and all adaptations preserved fidelity to PREVENT. Adaptations were made to content and features of the PREVENT tool and may have positive implications for equity that will be tested in future trials.</p><p><strong>Conclusion: </strong>Engaging rural partners to adapt our digital health tool prior to implementation with rural adults was critical to meet the unique needs of rural, low-income adult patients, fit the rural clinical care settings, and increase the likelihood of generating the intended impact among this patient population. The digital health expansion of FRAME can be applied prospectively or retrospectively by researchers and practitioners to plan, understand, and characterize digital health adaptations. This can aid intervention design, scale up, and evaluation in the rapidly expanding area of digital health.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1493814"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560351","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}