Frontiers in digital health最新文献

{"title":"Greening healthcare and slashing carbon emissions through telemedicine: a cross-sectional study from over 50 thousand remote consults at a leading tertiary hospital.","authors":"Roberto Nunes Umpierre, Rita Mattiello, Carlos André Aita Schmitz, Enrique Falceto de Barros, Rodolfo Souza da Silva, Marcelo Rodrigues Gonçalves, José Roberto Goldim","doi":"10.3389/fdgth.2025.1497770","DOIUrl":"10.3389/fdgth.2025.1497770","url":null,"abstract":"<p><strong>Introduction: </strong>Minimizing healthcare systems' resource footprints is crucial. To expand this focus, our objective was to assess the carbon emission reductions achievable through the introduction of telemedicine services at a prominent Brazilian tertiary hospital.</p><p><strong>Methods: </strong>This cross-section study included all patients who had remotely held appointments in a Brazilian tertiary hospital. The primary outcome was carbon emissions. The estimated carbon emissions were first calculated based on the distance between the hospital and the patient's home address. After, the calculated distance was multiplied by the amount of carbon estimated according to the type of transport used.</p><p><strong>Results: </strong>The study included 28,244 patients undergoing 52,878 remote appointments between March and December 2020, residing in 417 municipalities in Rio Grande do Sul and 80 towns in other Brazilian states. The total sum of distances and carbon gas reduction saved with the implementation of remote consultations amounted to 805,252.00 km and 939,641.94 kg of CO₂ emissions, respectively.</p><p><strong>Discussion: </strong>Telemedicine initiatives implemented in tertiary hospitals for less than a year result in a large amount of greenhouse gas emissions saved. Telemedicine emerges as a promising strategy with significant potential to mitigate the impact on planetary health.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1497770"},"PeriodicalIF":3.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303829","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":"Machine learning and explainable artificial intelligence to predict and interpret lead toxicity in pregnant women and unborn baby.","authors":"Priyanka Chaurasia, Pratheepan Yogarajah, Abbas Ali Mahdi, Sally McClean, Mohammad Kaleem Ahmad, Tabrez Jafar, Sanjay Kumar Singh","doi":"10.3389/fdgth.2025.1608949","DOIUrl":"10.3389/fdgth.2025.1608949","url":null,"abstract":"<p><strong>Introduction: </strong>Lead toxicity is a well-recognised environmental health issue, with prenatal exposure posing significant risks to infants. One major pathway of exposure to infants is maternal lead transfer during pregnancy. Therefore, accurately characterising maternal lead levels is critical for enabling targeted and personalised healthcare interventions. Current detection methods for lead poisoning are based on laboratory blood tests, which are not feasible for the screening of a wide population due to cost, accessibility, and logistical constraints. To address this limitation, our previous research proposed a novel machine learning (ML)-based model that predicts lead exposure levels in pregnant women using sociodemographic data alone. However, for such predictive models to gain broader acceptance, especially in clinical and public health settings, transparency and interpretability are essential.</p><p><strong>Methods: </strong>Understanding the reasoning behind the predictions of the model is crucial to building trust and facilitating informed decision-making. In this study, we present the first application of an explainable artificial intelligence (XAI) framework to interpret predictions made by our ML-based lead exposure model.</p><p><strong>Results: </strong>Using a dataset of 200 blood samples and 12 sociodemographic features, a Random Forest classifier was trained, achieving an accuracy of 84.52%.</p><p><strong>Discussion: </strong>We applied two widely used XAI methods, SHAP (SHapley additive explanations) and LIME (Local Interpretable Model-Agnostic Explanations), to provide insight into how each input feature contributed to the model's predictions.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1608949"},"PeriodicalIF":3.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303830","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":"Synthetic4Health: generating annotated synthetic clinical letters.","authors":"Libo Ren, Samuel Belkadi, Lifeng Han, Warren Del-Pinto, Goran Nenadic","doi":"10.3389/fdgth.2025.1497130","DOIUrl":"10.3389/fdgth.2025.1497130","url":null,"abstract":"<p><p>Clinical letters contain sensitive information, limiting their use in model training, medical research, and education. This study aims to generate reliable, diverse, and de-identified synthetic clinical letters to support these tasks. We investigated multiple pre-trained language models for text masking and generation, focusing on Bio_ClinicalBERT, and applied different masking strategies. Evaluation included qualitative and quantitative assessments, downstream named entity recognition (NER) tasks, and clinically focused evaluations using BioGPT and GPT-3.5-turbo. The experiments show: (1) encoder-only models perform better than encoder-decoder models; (2) models trained on general corpora perform comparably to clinical-domain models if clinical entities are preserved; (3) preserving clinical entities and document structure aligns with the task objectives; (4) Masking strategies have a noticeable impact on the quality of synthetic clinical letters: masking stopwords has a positive impact, while masking nouns or verbs has a negative effect; (5) The BERTScore should be the primary quantitative evaluation metric, with other metrics serving as supplementary references; (6) Contextual information has only a limited effect on the models' understanding, suggesting that synthetic letters can effectively substitute real ones in downstream NER tasks; (7) Although the model occasionally generates hallucinated content, it appears to have little effect on overall clinical performance. Unlike previous research, which primarily focuses on reconstructing original letters by training language models, this paper provides a foundational framework for generating diverse, de-identified clinical letters. It offers a direction for utilizing the model to process real-world clinical letters, thereby helping to expand datasets in the clinical domain. Our codes and trained models are available at https://github.com/HECTA-UoM/Synthetic4Health.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1497130"},"PeriodicalIF":3.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12163008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in clinical decision support and the prediction of adverse events.","authors":"S P Oei, T H G F Bakkes, M Mischi, R A Bouwman, R J G van Sloun, S Turco","doi":"10.3389/fdgth.2025.1403047","DOIUrl":"10.3389/fdgth.2025.1403047","url":null,"abstract":"<p><p>This review focuses on integrating artificial intelligence (AI) into healthcare, particularly for predicting adverse events, which holds potential in clinical decision support (CDS) but also presents significant challenges. Biases in data acquisition, such as population shifts and data scarcity, threaten the generalizability of AI-based CDS algorithms across different healthcare centers. Techniques like resampling and data augmentation are crucial for addressing biases, along with external validation to mitigate population bias. Moreover, biases can emerge during AI training, leading to underfitting or overfitting, necessitating regularization techniques for balancing model complexity and generalizability. The lack of interpretability in AI models poses trust and transparency issues, advocating for transparent algorithms and requiring rigorous testing on specific hospital populations before implementation. Additionally, emphasizing human judgment alongside AI integration is essential to mitigate the risks of deskilling healthcare practitioners. Ongoing evaluation processes and adjustments to regulatory frameworks are crucial for ensuring the ethical, safe, and effective use of AI in CDS, highlighting the need for meticulous attention to data quality, preprocessing, model training, interpretability, and ethical considerations.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1403047"},"PeriodicalIF":3.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303805","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":"\"Voice is the New Blood\": a discourse analysis of voice AI health-tech start-up websites.","authors":"Alden Blatter, Hortense Gallois, Emily Evangelista, Yael Bensoussan, Jean-Christophe Bélisle-Pipon","doi":"10.3389/fdgth.2025.1568159","DOIUrl":"10.3389/fdgth.2025.1568159","url":null,"abstract":"<p><strong>Introduction: </strong>Voice as a biomarker has emerged as a transformative field in health technology, providing non-invasive, accessible, and cost-effective methods for detecting, diagnosing, and monitoring various conditions. Start-ups are at the forefront of this innovative field, developing and marketing clinical voice AI solutions to a range of healthcare actors and shaping the field's early development. However, there is limited understanding of how start-ups in this field frame their innovations, and address-or overlook-critical socio-ethical, technical, and regulatory challenges in the rapidly evolving field of digital health.</p><p><strong>Methods: </strong>This study uses discourse analysis to examine the language on the public websites of 25 voice AI health-tech start-ups. Grounded in constitutive discourse analysis, which asserts that discourse both reflects and shapes realities, the study identifies patterns in how these companies describe their identities, technologies, and datasets.</p><p><strong>Results: </strong>The analysis shows start-ups consistently highlight the efficacy, reliability, and safety of their technologies, positioning them as transformative healthcare solutions. However, descriptions of voice datasets used to train algorithms vary widely and are often absent, reflecting broader gaps in acoustic and ethical standards for voice data collection and insufficient incentives for start-ups to disclose key data details.</p><p><strong>Discussion: </strong>Start-ups play a crucial role in the research, development, and marketization of voice AI health-tech, prefacing the integration of this new technology into healthcare systems. By publicizing discourse around voice AI technologies at this early stage, start-ups are shaping public perceptions, setting expectations for end-users, and ultimately influencing the implementation of voice AI technologies in healthcare. Their discourse seems to strategically present voice AI health-tech as legitimate by using promissory language typical in the digital health field and showcase the distinctiveness from competitors. This analysis highlights how this double impetus often drives narratives that prioritize innovation over transparency. We conclude that the lack of incentive to share key information about datasets is due to contextual factors that start-ups cannot control, mainly the absence of clear standards and regulatory guidelines for voice data collection. Addressing these complexities is essential to building trust and ensuring responsible integration of voice AI into healthcare systems.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1568159"},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12158947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287345","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":"Exploring user experiences of clinicians engaged with the digital healthcare interventions across the referral and university teaching hospitals in Nigeria: a qualitative study.","authors":"Uchechukwu Solomon Onyeabor, Okechukwu Onwuasoigwe, Wilfred Okwudili Okenwa, Thorsten Schaaf, Niels Pinkwart, Felix Balzer","doi":"10.3389/fdgth.2025.1488880","DOIUrl":"10.3389/fdgth.2025.1488880","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Given that Nigeria and several developing countries are still at the early stage of digital healthcare interventions adoption (like the use of electronic health records systems) there is scarcity of research/empirical reports investigating the overall user experiences of clinicians, the doctors and the nurses who are or who had been practically engaged with the use of these new digital healthcare support implementations that had engendered new culture across their care delivery facilities. The referral and university teaching hospitals in Nigeria numbering over 166 and scattered across over 37 states of the federation and the Federal Capital Territory (FCT) make up a strategic component of Nigeria's healthcare ecosystem. This research was therefore designed and restricted to clinicians who had used these systems so as to explore their experiences with these systems and possibly unveil any challenges/limitations that can bedevil successful and sustainable acquisition of digital healthcare intervention programmes and projects across referral and university teaching hospitals in the Southeastern Region of Nigeria; and could also hamper any future implementations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Method: &lt;/strong&gt;This study was designed in a manner that allows the clinicians the liberty to conveniently express in writing (via comments) issues, challenges and concerns that they had perceived, encountered or experienced bedevilling electronic health record adoption and use across their care facilities. So a structured interview method was chosen by the research team (after due considerations) as fitting the research context. This (structured interview) was therefore designed and targeted at about 400 clinical participants, including the doctors and the nurses from three select referral and university teaching hospitals in the Southeastern Region of Nigeria (a federal, state, and national specialist referral hospital).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Result: &lt;/strong&gt;Out of the 400 clinicians who were targeted in the survey, 326 of them practically responded to the interview questions. The outcome showed the clinicians willingly exposing several issues and challenges that had stifled electronic health record adoption across the hospitals. Issues identified were categorized into themes including challenges bordering on lack of political will on the part of hospital administration; lack of computer/digital/EHR literacy; poor and often lack of comprehensive training on the workings of EHR; poor maintenance culture; poor EHR system design, poor implementation and use-based struggles and challenges; infrastructure issues, system breakdown and network challenges etc were reported.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The outcome of this investigation has profoundly exposed practical issues that had hitherto stifled and often suffocated electronic health record implementation projects across referral and university teaching hospitals in Nigeria. And given the strat","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1488880"},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12158922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287347","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":"Human-centered design for smart home technologies: a framework for aging and mental health.","authors":"Mohammad Mahdi Fakhimi, Adriana Hughes, Allison M Gustavson","doi":"10.3389/fdgth.2025.1555569","DOIUrl":"10.3389/fdgth.2025.1555569","url":null,"abstract":"<p><p>Smart home technologies (SHTs) offer promising ways to support older adults with both mobility challenges and mental health needs, yet high costs, complex interfaces, and uncertain data practices often limit adoption. This paper addresses these challenges by proposing a human-centered design (HCD) framework focused on affordability, inclusive design for physical and cognitive variations, and transparent data governance. Through illustrative examples of low-cost sensor networks and culturally tailored voice interfaces, we argue that thoughtfully designed SHTs can promote independent living, strengthen mental health interventions, and foster user trust. We conclude by highlighting policy incentives and cross-sector collaboration as critical levers for making SHTs an accessible, sustainable tool for aging populations.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1555569"},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12158959/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287348","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":"Balancing risks and benefits: clinicians' perspectives on the use of generative AI chatbots in mental healthcare.","authors":"Lyndsey Hipgrave, Jessie Goldie, Simon Dennis, Amanda Coleman","doi":"10.3389/fdgth.2025.1606291","DOIUrl":"10.3389/fdgth.2025.1606291","url":null,"abstract":"<p><strong>Introduction: </strong>The use of generative-AI chatbots has proliferated in mental health, to support both clients and clinicians across a range of uses. This paper aimed to explore the perspectives of mental health clinicians regarding the risks and benefits of integrating generative-AI chatbots into the mental health landscape.</p><p><strong>Methods: </strong>Twenty-three clinicians participated in a 45-minute virtual interview, in which a series of open-ended and scale-based questions were asked, and a demonstration of a mental health chatbot's potential capabilities was presented.</p><p><strong>Results: </strong>Participants highlighted several benefits of chatbots, such as their ability to administer homework tasks, provide multilingual support, enhance accessibility and affordability of mental healthcare, offer access to up-to-date research, and increase engagement in some client groups. However, they also identified risks, including the lack of regulation, data and privacy concerns, chatbots' limited understanding of client backgrounds, potential for client over-reliance on chatbots, incorrect treatment recommendations, and the inability to detect subtle communication cues, such as tone and eye contact. There was no significant finding to suggest that participants viewed either the risks or benefits as outweighing the other. Moreover, a demonstration of potential chatbot capabilities was not found to influence whether participants favoured the risks or benefits of chatbots.</p><p><strong>Discussion: </strong>Qualitative responses revealed that the balance of risks and benefits is highly contextual, varying based on the use case and the population group being served. This study contributes important insights from critical stakeholders for chatbot developers to consider in future iterations of AI tools for mental health.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1606291"},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12158938/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287346","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":"Leveraging machine learning in nursing: innovations, challenges, and ethical insights.","authors":"Sophie So Wan Yip, Sheng Ning, Niki Yan Ki Wong, Jeffrey Chan, Kei Shing Ng, Bernadette Oi Ting Kwok, Robert L Anders, Simon Ching Lam","doi":"10.3389/fdgth.2025.1514133","DOIUrl":"10.3389/fdgth.2025.1514133","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Aim/objective: &lt;/strong&gt;This review aims to provide a comprehensive analysis of the integration of machine learning (ML) (1) in nursing by exploring its implications on patient care, nursing practices, and healthcare delivery. It highlights current applications, challenges, ethical considerations, and the potential future developments of ML in nursing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;With the advent of ML in healthcare, the nursing profession stands on the cusp of a transformative era. Despite the technological advancements, discussions on the utilization of ML in nursing, which are crucial for advancing the profession, are lacking. This review seeks to fill this gap by examining the balance between technological innovation and the human-centric nature of nursing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Design: &lt;/strong&gt;This narrative review employs a detailed search strategy across several databases, including PubMed, Embase, MEDLINE, Scopus, and Web of Science. It focuses on articles that were published from January 2019 to December 2023. Moreover, this review aims to illustrate the current use, challenges, and future potential of ML applications in nursing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Inclusion criteria targeted articles that focus on ML application in nursing, challenges, ethical considerations, and future directions. Exclusion criteria omitted opinion pieces and nonrelevant studies. Articles were categorized into themes, such as patient care, nursing education, operational efficiency, ethical considerations, and future potential, thus facilitating a structured analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Findings demonstrate that ML has significantly enhanced patient monitoring, predictive analytics, and preventive care. For example, the COMPOSER deep learning model for early sepsis prediction was associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality and a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance. In nursing education, ML has improved simulation-based training by facilitating adaptive learning experiences that support continual skill development. Furthermore, ML contributes to operational efficiency through automated staffing optimization and administrative task automation, thus reducing nurse workload and enhancing patient care outcomes. However, key challenges include ethical considerations, such as data privacy, algorithmic bias, and patient autonomy, which necessitate ongoing research and regulatory oversight.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;ML in nursing offers transformative potential across patient care, education, and operational efficiency, which is balanced by significant challenges and ethical considerations. Future directions include expanding clinical and community applications, integrating emerging technologies, and enhancing nursing education. Continuous research, ethical oversight, and interdisciplinary collaboration are essential for harnessing ML's full ","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1514133"},"PeriodicalIF":3.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141303/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251085","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":"Co-design of a digital 24-hour time-use intervention with older adults and allied health professionals.","authors":"Henry T Blake, Aaron Davis, Maddison L Mellow, Melissa Hull, Bethany Robins, Kate Laver, Dorothea Dumuid, Timothy Olds, Hannah A D Keage, Lui Di Venuto, Ashleigh E Smith","doi":"10.3389/fdgth.2025.1544489","DOIUrl":"10.3389/fdgth.2025.1544489","url":null,"abstract":"<p><p>Using co-design principles based on the Health CASCADE framework, we aimed to describe the collaborative process undertaken to develop a 24-hour time-use intervention, called Small Steps, which promoted gradual and incremental health-behavior change. A secondary aim was to reflect on the challenges and benefits of co-design in this project, offering insights into the \"why\" and \"how\" to co-design 24-hour time-use interventions with priority populations. Twelve participants were invited and participated in 6 co-design workshops (June 2023-January 2024). To prioritize older adults' views in the end-product, 8 adults aged >65 years (the target population) and 4 allied health professionals with >2 years' experience working with the target population were recruited. Workshops and activities were structured using the British Design Council's Double Diamond Design Process to stimulate design thinking. Where possible, participant-led documentation was used to reduce the bias associated with academic scribing and empower participants to provide input and facilitate ownership for the project. Workshop activities and discussions were captured through printouts, audio and iPad screen recordings and analyzed through reflexive thematic and content analysis. Co-designers contributed to all elements of the intervention including the website design, the content, and the level of researcher input during the intervention. Iterative improvements were made based on the unique perspectives and needs of the community experts. During the action planning process, older adults wanted both support and autonomy, while maintaining the freedom to adapt these options to their individual needs. Older adults also preferred a step-by-step approach, allowing for gradual behavior changes across the intervention to avoid feelings of becoming overwhelmed. The co-design process enabled the tailoring of the Small Steps intervention to the specific needs of its intended audience. Key factors contributing to the co-design included flexibility in the design process, fostering a supportive environment, and empowering participants through activities that guided and stimulated their thinking. These elements not only helped shape the development of Small Steps but reinforced the value of co-design in developing personalised interventions for older adults.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1544489"},"PeriodicalIF":3.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251084","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}