DIGITAL HEALTH最新文献

{"title":"Perceptions regarding cardiovascular health and preparedness for digital health education among adolescents in an urban community of Nepal: A qualitative study.","authors":"Dayana Shakya, Karin Flodin, Dip Raj Thapa, Madhusudan Subedi, Nawi Ng, Abhinav Vaidya, Natalia Oli, Alexandra Krettek","doi":"10.1177/20552076251321068","DOIUrl":"10.1177/20552076251321068","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular disease (CVD) is the leading cause of death in Nepal. As CVD risks can develop early in life, a life course approach for non-communicable disease (NCD) prevention is needed. Due to its potentially acceptable delivery mode, digital health education could be a promising way forward to increase adolescents' CVD knowledge.</p><p><strong>Purpose: </strong>The purpose of this study was to explore adolescents' CVD perceptions and their perceptions and preparedness for digital cardiovascular health education through mobile games.</p><p><strong>Methods: </strong>Twelve focus group discussions were conducted with adolescents, Grades 8-10, from two public and four private Nepalese schools. A qualitative study with a deductive thematic analysis was performed, guided by the health belief model (HBM) and the technology acceptance model (TAM).</p><p><strong>Results: </strong>The analysis resulted in 6 themes and 13 sub-themes concerning perceptions of CVD and 5 themes and 10 sub-themes on perceptions and preparedness for digital cardiovascular health education through mobile games. The adolescents viewed CVD as a serious disease with consequences. A healthy diet and physical activity were important for prevention. Benefits were the positive impacts of a heart-healthy lifestyle. Barriers were the temptation of consuming unhealthy food, lack of healthy food environments, time and motivation. The adolescents also stressed their own ability to prevent CVD. Digital cardiovascular health education through mobile games was desirable. Constraints were accessibility and technical issues, parental allowance, available time and whether the game was engrossing enough.</p><p><strong>Conclusion: </strong>The adolescents perceived CVD as serious, with benefits and barriers connected to its prevention. Digital cardiovascular health education through mobile games was viewed positively but not without constraints for successful implementation.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251321068"},"PeriodicalIF":2.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11837072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy of a physical rehabilitation program using virtual reality in patients with chronic tendinopathy: A randomized controlled trial protocol (VirTendon-Rehab).","authors":"David Lucena-Anton, Juan G Dominguez-Romero, Juan C Chacon-Barba, María José Santi-Cano, Carlos Luque-Moreno, Jose A Moral-Munoz","doi":"10.1177/20552076241297043","DOIUrl":"10.1177/20552076241297043","url":null,"abstract":"<p><strong>Objectives: </strong>To analyze the efficacy of a virtual reality (VR)-based rehabilitation program in people with chronic tendinopathy (CT) on pain, muscle activation pattern, range of motion, muscle strength, kinesiophobia, physical function, quality of life, and user satisfaction compared to a control group. In addition, the relationship between these variables and the clinical profile of this population will be analyzed.</p><p><strong>Design: </strong>A 12-week, single-blind, low-risk, randomized controlled trial.</p><p><strong>Methods: </strong>Sixty patients diagnosed with CT will be enrolled and randomly assigned to two groups. The control group will receive a physical exercise program without VR support (45 min), whereas the experimental group will receive an additional 15-min intervention through a physical exercise program delivered by VR. Both groups will receive three sessions per week, and the outcomes will be collected at baseline, after 12 weeks, and at the 24-week follow-up. Stratified groups will be established according to tendinopathy location (shoulder rotator cuff, elbow, patella, and Achilles tendon). Statistical analyses using SPSS v.24 will include descriptive analysis, stratified analysis by tendinopathy location, normality checks, intragroup and intergroup differences, effect sizes, and variable relationships.</p><p><strong>Discussion: </strong>The results of this project may have a significant impact on the knowledge of using VR in tendinopathy management, understanding how the outcomes are related, and characterizing the clinical profiles of the population diagnosed with CT. If these results are confirmed, VR would be clinically useful for the treatment of these conditions.</p><p><strong>Trial registration number: </strong>NCT06056440.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076241297043"},"PeriodicalIF":2.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11837136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging BiLSTM-CRF and adversarial training for sentiment analysis in nature-based digital interventions: Enhancing mental well-being through MOOC platforms.","authors":"Juanjuan Zang","doi":"10.1177/20552076251317345","DOIUrl":"10.1177/20552076251317345","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to leverage annotated textual data from a Massive Open Online Course (MOOC) platform to conduct sentiment analysis of learners' interactions with nature-based digital interventions, which seeks to enhance sentiment classification and provide insights into learners' affective experiences, ultimately facilitating timely psychological interventions and improving curriculum design.</p><p><strong>Methods: </strong>This study leverages the extensive corpus of annotated textual data available on a MOOC platform, encompassing learners' assessments, inquiries, and recommendations. By performing meticulous sentiment analysis, we aim to understand the subjective sentiments of learners engaging with nature-based digital interventions. To achieve this, we integrate a Bidirectional Long Short-Term Memory (BiLSTM) network with a Conditional Random Field (CRF). The BiLSTM captures word associations in both forward and backward directions, feeding these results into the CRF network to establish the conditional distribution between the feature function and labels. This ensures high-quality feature extraction, precise label assignment, and the derivation of evaluation metrics. Furthermore, adversarial training is introduced to enhance aspect sentiment classification. This involves incorporating perturbations in the embedding space, generating adversarial samples at the embedding layer and semantic feature fusion layer, and combining these with the original samples for model training.</p><p><strong>Results: </strong>Experimental outcomes demonstrate that the proposed model achieves precision, recall, and F1 scores of 83.71, 85.66, and 84.67 on the SemEval-2014 dataset, and 80.63, 83.06, and 81.76 on the Coursera dataset.</p><p><strong>Conclusion: </strong>Notably, the sentiment prediction efficacy surpasses that of comparative models, underscoring the proficiency of the proposed scheme. By harnessing the proposed model, educators and administrators can effectively sift through learners' affective information, facilitating timely psychological interventions and curriculum guidance. This study contributes to the growing body of research on digital mental health interventions within natural settings, providing valuable insights into how technology can support and enhance mental well-being in these contexts.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251317345"},"PeriodicalIF":2.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11837057/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional characteristics of sleep monitoring devices in China: A real-world cross-sectional study.","authors":"Le Yang, Bingtao Weng, Xingyan Xu, Zhi Huang, Run Ding, Miaomiao Si, Yingxin Fu, Yurui Zhu, Yu Jiang, Beibei Rao, Xinyi Zhang, Qingwei Zhou, Shenglan Lin, Yansong Guo, XiaoXu Xie","doi":"10.1177/20552076251320752","DOIUrl":"10.1177/20552076251320752","url":null,"abstract":"<p><strong>Background: </strong>Sleep monitoring devices present potential improvements to address the challenges of sleep disorders. However, systematic evaluations are lacking. This study investigates the functional characteristics of existing sleep monitoring devices in the Chinese market and delves into population preferences.</p><p><strong>Objective: </strong>We aim to summarize the characteristics of mobile health devices with sleep monitoring function in China, analyzing product features and market prices, and collect population preferences for mobile health devices, providing a concrete basis for the ongoing development of mobile health technologies.</p><p><strong>Methods: </strong>Data on 203 sleep devices were gathered from four major mobile shopping platforms (Tmall, JD.com, Pinduoduo, and Suning) using relevant keywords. A two-level variance model was employed to analyzed the link between device features and sales. Additionally, a structured questionnaire assessed public usage and attitudes towards these devices, with 167 responses collected via social networks.</p><p><strong>Results: </strong>Our study found that smart bracelets, which make up 82.6% of sleep monitoring devices, effectively track heart rate, physical activity blood oxygen saturation, sleep duration, and assess sleep quality. Most devices cost under 500 RMB, influencing sales (β<i> </i>= -1.111 to -3.490, <i>p </i>< 0.001-.002). Features such as sleep quality assessment (β=0.520, <i>p </i>= 0.03), measuring physical activity (β=0.464, <i>p </i>= 0.03) and blood oxygen saturation (β=0.465, <i>p </i>= 0.02) are significantly associated with higher sales volumes. Usage peaks during and after exercise (41.0%) and during sleep (21.3%), with 60.4% preferring to spend under 500 RMB.</p><p><strong>Conclusions: </strong>The study confirms a strong preference for smart bracelets with health tracking features, particularly for sleep monitoring, at a price point below 500 RMB. These findings highlight the potential of affordable, multifunctional devices to shape the future of smart healthcare, especially through cloud-based enhancements that improve doctor-patient communication.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251320752"},"PeriodicalIF":2.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11837058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACU-Net: Attention-based convolutional U-Net model for segmenting brain tumors in fMRI images.","authors":"Md Alamin Talukder, Md Abu Layek, Md Aslam Hossain, Md Aminul Islam, Mohammad Nur-E-Alam, Mohsin Kazi","doi":"10.1177/20552076251320288","DOIUrl":"10.1177/20552076251320288","url":null,"abstract":"<p><strong>Objective: </strong>Accurate segmentation of brain tumors in medical imaging is essential for diagnosis and treatment planning. Current techniques often struggle with capturing complex tumor features and are computationally demanding, limiting their clinical application. This study introduces the attention-based convolutional U-Net (ACU-Net) model, designed to improve segmentation accuracy and efficiency in fMRI images by incorporating attention mechanisms that selectively highlight critical features while preserving spatial context.</p><p><strong>Methods: </strong>The ACU-Net model combines convolutional neural networks (CNNs) with attention mechanisms to enhance feature extraction and spatial coherence. We evaluated ACU-Net on the BraTS 2018 and BraTS 2020 fMRI datasets using rigorous data splitting for training, validation, and testing. Performance metrics, particularly Dice scores, were used to assess segmentation accuracy across different tumor regions, including whole tumor (WT), tumor core (TC), and enhancing tumor (ET) classes.</p><p><strong>Results: </strong>ACU-Net demonstrated high segmentation accuracy, achieving Dice scores of 99.23%, 99.27%, and 96.99% for WT, TC, and ET, respectively, on the BraTS 2018 dataset, and 98.72%, 98.40%, and 97.66% for WT, TC, and ET on the BraTS 2020 dataset. These results indicate that ACU-Net effectively captures tumor boundaries and subregions with precision, surpassing traditional segmentation approaches.</p><p><strong>Conclusion: </strong>The ACU-Net model shows significant potential to enhance clinical diagnosis and treatment planning by providing precise and efficient brain tumor segmentation in fMRI images. The integration of attention mechanisms within a CNN architecture proves beneficial for identifying complex tumor structures, suggesting that ACU-Net can be a valuable tool in medical imaging applications.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251320288"},"PeriodicalIF":2.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge mapping of online healthcare: An interdisciplinary visual analysis using VOSviewer and CiteSpace.","authors":"Xue Ding, Dingming Lu, Ruoxi Wei, Fangfang Zhu","doi":"10.1177/20552076251320761","DOIUrl":"10.1177/20552076251320761","url":null,"abstract":"<p><strong>Background: </strong>Online healthcare has been regarded as a permanent component and complementation in routine worldwide healthcare. Although there have been large-scale related studies in this field, studies are scattered across disciplines. Numerous publications are needed to systematically and comprehensively identify the status quo, development, and future hotspots in this field.</p><p><strong>Methods: </strong>Publications on online healthcare were screened from the WoS database. By using VOSviewer and CiteSpace, this study analyzed 4636 articles in this field with 60,306 associated references. First, countries/regions distributions, institutions distributions, influential journals, and productive authors were obtained. Then, co-citation analysis, co-occurrence analysis, timeline analysis, and burst detection were further conducted to sketch the panorama of online healthcare.</p><p><strong>Results: </strong>There were 147 countries/regions participated in and contributed to this field in total. Accounting for over half of the total number of publications, the USA, England, Australia, China, and Canada played significant roles in this area. Among the 24,362 authors, Guo XT was the most influential author. The International Journal of Environmental Research and Public Health was the journal with the most publications and citations. Studies in this field have shifted from basic research to applied practice research. COVID-19, mental health, healthcare, and healthcare workers were the most common keywords, indicating that studies on the impact of online healthcare on healthcare workers, online healthcare service for COVID-19, and mental health will be promising areas in the future.</p><p><strong>Conclusions: </strong>Research on online healthcare is booming, while worldwide cooperation is still regionalized. Cross-regional cooperation among institutions and scholars is needed to enhance in the future. Online healthcare services for specific health fields and specific groups are the current and developing topics in this field.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251320761"},"PeriodicalIF":2.9,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11831660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Barriers to and enhancement of the utilization of digital mental health interventions in low-resource settings: Perceptions of young people in Uganda.","authors":"Erisa Sabakaki Mwaka, Datsun Bazzeketa, Joy Mirembe, Reagan D Emoru, Adelline Twimukye, Apollo Kivumbi","doi":"10.1177/20552076251321698","DOIUrl":"10.1177/20552076251321698","url":null,"abstract":"<p><strong>Introduction: </strong>Digital mental health (DMH) enhances access to healthcare, particularly in low- and middle-income countries where investment in mental healthcare is low. However, utilization among young people (YP) is low. This study aimed to explore YP's perceptions of the barriers to the using of DMH interventions in low-resource settings.</p><p><strong>Methods: </strong>A qualitative descriptive approach was used. Six face-to-face focus group discussions were conducted with 50 YP from nine universities in Uganda. The median age was 24 years (range 21-25 years) and respondents were drawn from diverse academic programmes with the majority being medical students (54%). A thematic approach was used to interpret the results.</p><p><strong>Results: </strong>Three themes were identified from the data including perceptions of using DMH services, the perceived barriers to utilization, and suggestions for enhancement of DMH for YP in low-resource settings. Most respondents had a positive attitude towards DMH. The perceived barriers to utilization of DMH included the fear of stigma, affordability, inequitable access, privacy and confidentiality concerns, and app-related challenges. Access and use of DMH can be enhanced through public engagement, creating awareness, enhanced training, and access to affordable DMH interventions.</p><p><strong>Conclusion: </strong>DMH was deemed important in extending healthcare to YP, particularly in health systems where traditional mental health services are not readily available. However, several factors hinder equitable access to DMH in low-resource settings. There is a need for long-term investment in digital health technologies.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251321698"},"PeriodicalIF":2.9,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11831655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRpoxNet: An enhanced deep learning approach for early detection of monkeypox using modified ResNet50.","authors":"Vandana, Chetna Sharma, Mohd Asif Shah","doi":"10.1177/20552076251320726","DOIUrl":"10.1177/20552076251320726","url":null,"abstract":"<p><strong>Objective: </strong>To develop an enhanced deep learning model, MRpoxNet, based on a modified ResNet50 architecture for the early detection of monkeypox from digital skin lesion images, ensuring high diagnostic accuracy and clinical reliability.</p><p><strong>Methods: </strong>The study utilized the Kaggle MSID dataset, initially comprising 1156 images, augmented to 6116 images across three classes: monkeypox, non-monkeypox, and normal skin. MRpoxNet was developed by extending ResNet50 from 177 to 182 layers, incorporating additional convolutional, ReLU, dropout, and batch normalization layers. Performance was evaluated using metrics such as accuracy, precision, recall, F1 score, sensitivity, and specificity. Comparative analyses were conducted against established models like ResNet50, AlexNet, VGG16, and GoogleNet.</p><p><strong>Results: </strong>MRpoxNet achieved a diagnostic accuracy of 98.1%, outperforming baseline models in all key metrics. The enhanced architecture demonstrated superior robustness in distinguishing monkeypox lesions from other skin conditions, highlighting its potential for reliable clinical application.</p><p><strong>Conclusion: </strong>MRpoxNet provides a robust and efficient solution for early monkeypox detection. Its superior performance suggests readiness for integration into diagnostic workflows, with future enhancements aimed at dataset expansion and multimodal adaptability to diverse clinical scenarios.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251320726"},"PeriodicalIF":2.9,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11863262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 recognition from chest X-ray images by combining deep learning with transfer learning.","authors":"Chang-Jiang Zhang, Lu-Ting Ruan, Ling-Feng Ji, Li-Li Feng, Fu-Qin Tang","doi":"10.1177/20552076251319667","DOIUrl":"10.1177/20552076251319667","url":null,"abstract":"<p><strong>Objective: </strong>Based on the current research status, this paper proposes a deep learning model named Covid-DenseNet for COVID-19 detection from CXR (computed tomography) images, aiming to build a model with smaller computational complexity, stronger generalization ability, and excellent performance on benchmark datasets and other datasets with different sample distribution features and sample sizes.</p><p><strong>Methods: </strong>The proposed model first extracts and obtains features of multiple scales from the input image through transfer learning, followed by assigning internal weights to the extracted features through the attention mechanism to enhance important features and suppress irrelevant features; finally, the model fuses these features of different scales through the multi-scale fusion architecture we designed to obtain richer semantic information and improve modeling efficiency.</p><p><strong>Results: </strong>We evaluated our model and compared it with advanced models on three publicly available chest radiology datasets of different types, one of which is the baseline dataset, on which we constructed the model Covid-DenseNet, and the recognition accuracy on this test set was 96.89%, respectively. With recognition accuracy of 98.02% and 96.21% on the other two publicly available datasets, our model performs better than other advanced models. In addition, the performance of the model was further evaluated on external test sets, trained on data sets with balanced sample distribution (experiment 1) and unbalanced sample distribution (experiment 2), identified on the same external test set, and compared with DenseNet121. The recognition accuracy of the model in experiment 1 and experiment 2 is 80% and 77.5% respectively, which is 3.33% and 4.17% higher than that of DenseNet121 on external test set. On this basis, we also changed the number of samples in experiment 1 and experiment 2, and compared the impact of the change in the number of training set samples on the recognition accuracy of the model on the external test set. The results showed that when the number of samples increased and the sample features became more abundant, the trained Covid-DenseNet performed better on the external test set and the model became more robust.</p><p><strong>Conclusion: </strong>Compared with other advanced models, our model has achieved better results on multiple datasets, and the recognition effect on external test sets is also quite good, with good generalization performance and robustness, and with the enrichment of sample features, the robustness of the model is further improved, and it has better clinical practice ability.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251319667"},"PeriodicalIF":2.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11822832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an evidence-informed implementation strategy for a digital supportive care platform for brain tumour patients, their carers and healthcare professionals.","authors":"Verena Schadewaldt, Teresa O'Brien, Mahima Kalla, Meinir Krishnasamy, Kara Burns, Sarah Ce Bray, Cecily Gilbert, Richard De Abreu Lourenco, Joseph Thomas, Daniel Capurro, Wendy Chapman, Ann Borda, Rana S Dhillon, James R Whittle, Katharine J Drummond","doi":"10.1177/20552076251316713","DOIUrl":"10.1177/20552076251316713","url":null,"abstract":"<p><strong>Background: </strong>Implementation challenges of digital health solutions (DHSs) comprise complexities of behavioural change, resource limitation, inertia in existing systems, and failure to include consumer preferences. Understanding the factors which contribute to successful implementation of DHS is essential. We report the development of an implementation strategy for Brain Tumours Online (BT Online), a digital supportive care platform for patients with brain tumours, their carers and healthcare professionals.</p><p><strong>Aim: </strong>To develop an evidence-informed implementation strategy for BT Online, considering the specific barriers and facilitators to implementing DHS for adults with a brain tumour and their carers and healthcare professionals.</p><p><strong>Methods: </strong>A rapid review methodology was used to summarise factors relevant to implementation of DHS for people affected by cancer. Themes from the review were supported by implementation guidelines for DHS and the combined evidence informed the implementation strategy. Each theme was matched with specific steps for implementing BT Online.</p><p><strong>Results: </strong>The rapid review identified 10 themes, namely, awareness of the new digital platform; institutional integration and support; data security, the quality, usability and accessibility of the platform; belief in the benefit of the platform; the need for holistic and tailored features; the timing of introducing the platform; engagement of healthcare professionals; and the re-definition of roles and workload. The themes were matched with 51 concrete implementation steps.</p><p><strong>Discussion: </strong>The purpose of the strategy was to minimise risk of implementation failure, consider the specific context of care and generate a reference framework to evaluate BT Online prior to upscaling to use outside the research context. Our method contributes a novel approach of developing an evidence-informed rigorous implementation strategy if existing implementation frameworks do not apply.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251316713"},"PeriodicalIF":2.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11822819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}