DIGITAL HEALTH最新文献

{"title":"Artificial intelligence in occupational therapy documentation: Chatbot vs. Occupational Therapists.","authors":"Si-An Lee, Jin-Hyuck Park","doi":"10.1177/20552076251386657","DOIUrl":"https://doi.org/10.1177/20552076251386657","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI)-based language models such as ChatGPT show promise in generating medical documentation. However, their effectiveness in occupational therapy (OT) documentation-particularly in terms of perceived quality and empathy-remains underexplored.</p><p><strong>Objective: </strong>This study aimed to compare the quality and empathy of clinical documentation generated by licensed occupational therapists versus ChatGPT-3.5, using standardized OT case scenarios.</p><p><strong>Methods: </strong>Fifteen standardized OT cases were used to generate human- and AI-written assessment and plan sections. Five occupational therapists and five patients or caregivers independently evaluated the documentation using 5-point Likert scales across three quality subdomains (completeness, correctness, concordance) and three empathy dimensions (cognitive, affective, behavioral). Inter-rater reliability and correlations between quality and empathy were also analyzed.</p><p><strong>Results: </strong>Artificial intelligence-generated documentation received significantly higher ratings across all quality and empathy dimensions than human-generated documentation (all <i>p</i> < 0.001). However, human-generated documentation demonstrated stronger correlations between quality and empathy, and higher inter-rater reliability, indicating greater consistency among evaluators. These findings suggest that while AI can produce responses perceived as more complete and empathetic, its outputs may vary more widely in interpretation.</p><p><strong>Conclusion: </strong>Artificial intelligence-based tools may help reduce documentation burdens for therapists by generating high-quality, empathetic notes. However, human-authored documentation remains more consistent across evaluators. These results underscore the potential and limitations of AI in clinical documentation, highlighting the need for further development to enhance contextual sensitivity, communication coherence, and evaluator reliability. Future research should examine AI performance in real-world OT practice settings.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251386657"},"PeriodicalIF":3.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12515345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145287626","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":"LiteFallNet: A lightweight deep learning model for efficient real-time fall detection.","authors":"Emmanuel Owusu, Isaac Acquah, Michael Asiedu Asare, Benjamin Appiah Yeboah","doi":"10.1177/20552076251386698","DOIUrl":"https://doi.org/10.1177/20552076251386698","url":null,"abstract":"<p><strong>Objective: </strong>This study introduces LiteFallNet, a lightweight and interpretable deep learning model for real-time fall detection using only inertial sensor data. It aims to overcome key limitations in current systems, including high computational demands, latency, and privacy concerns, while delivering accurate and reliable performance.</p><p><strong>Methods: </strong>LiteFallNet integrates a Gated Recurrent Unit (GRU) layer, a Temporal Convolutional Network (TCN) block, depthwise separable convolutions, and a Squeeze-and-Excitation (SE) block to efficiently extract temporal features from tri-axial accelerometer, gyroscope, and magnetometer signals. The model was trained and evaluated on the FallAllD and the UMAFall datasets. To enhance transparency, one-dimensional gradient-weighted class activation mapping (1D Grad-CAM) and local interpretable model-agnostic explanations (LIME) were used to interpret how the model made its predictions.</p><p><strong>Results: </strong>The model on the FallAllD dataset achieved an accuracy of 97.81%, a recall of 98.55%, and an F1-score of 97.88%, with an area under the receiver operating characteristic curve of 99.33%. With a size of just 0.312 MB and an inference time of 7.07 ms, LiteFallNet combines strong performance with efficiency. These attributes make it highly suitable for deployment in real-time, resource-constrained environments.</p><p><strong>Conclusion: </strong>LiteFallNet offers a privacy-preserving and real-time solution for fall detection. Its accuracy, transparency, and lightweight design make it suitable for smart homes, eldercare facilities, and wearable health technologies.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251386698"},"PeriodicalIF":3.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12515292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145287658","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":"Virtual reality and augmented reality in ophthalmology: A recent update.","authors":"Zahra Bibak-Bejandi, Alireza Razavi, Hanieh Niktinat, Zarife Jale Yucel, Aron M Sebhat, Reyhaneh Bibak-Bejandi, Zohre Arabpour, Anwar N Khandaker, Jaron Sanchez, Omar Nusair, Mohammad Soleimani","doi":"10.1177/20552076251387047","DOIUrl":"https://doi.org/10.1177/20552076251387047","url":null,"abstract":"<p><p>Virtual reality (VR) and augmented reality (AR) are three-dimensional (3D) environments designed to mimic the real world. They have shown significant applications in science, especially in medicine. Their use spans various areas, including education, surgical training, patient education, and medical instruction. In ophthalmology, which is considered a field of microsurgery, VR and AR provide a 3D environment for practicing precise procedures. The ability to repeat exercises, independence from real patients, and tactile feedback allow residents to practice numerous times in a risk-free setting. Learning in a 3D environment is not limited to surgical techniques but it also supports learning theoretical concepts and practicing patient history-taking, reducing dependence on traditional professional training. This approach allows students in low socioeconomic countries to access high-quality training platforms. Beyond medical education, VR and AR can also be used to educate patients in a simple and accessible way, helping them understand their pathophysiology. Raising awareness about signs and symptoms is an effective method to prevent the progression of eye conditions. In this narrative review, we discuss the applications of VR and AR in ophthalmology, their benefits, and future directions.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251387047"},"PeriodicalIF":3.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12515333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145287683","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":"Integrating wearable mobile health technologies into chronic heart failure management: Insights from a mixed-methods study and persona development.","authors":"Laura Svensson, Carolin Anders, Christoph Dieterich, Oliver Heinze, Petra Knaup, Lina Weinert","doi":"10.1177/20552076251375967","DOIUrl":"https://doi.org/10.1177/20552076251375967","url":null,"abstract":"<p><strong>Background: </strong>Chronic heart failure (CHF) affects over 64 million people globally and often reduces quality of life (QoL), contributing to higher mortality. Wearable devices offer opportunities for continuous monitoring and self-management. However, patient characteristics and perceptions of wearables vary, and healthcare practitioners (HCPs) lack guidance on identifying patients who would benefit from such tools. This study investigates patients' experiences with wearables for self-monitoring and develops personas to assist HCPs in tailoring CHF management.</p><p><strong>Methods: </strong>A mixed-methods approach was used, combining qualitative semi-structured interviews and quantitative QoL data via the Kansas City Cardiomyopathy Questionnaire (KCCQ-12). CHF patients received an Apple Watch and iPhone SE for tracking vital data and completing questionnaires and participated in semi-structured interviews. Descriptive analysis of KCCQ-12 scores and thematic analysis of interview transcripts informed the creation of patient personas based on previous findings.</p><p><strong>Results: </strong>Thematic analysis identified six main themes, including self-monitoring practices, barriers, and factors influencing acceptance. Most patients used wearables daily, reporting benefits like increased health awareness and improved communication with doctors. Barriers included technical issues and difficulty integrating study devices with personal ones. Quantitative analysis suggested a tendency toward higher QoL among interview participants. Four personas emerged, reflecting varying levels of motivation, literacy, and disease burden.</p><p><strong>Conclusion: </strong>Wearables show promise for improving CHF self-management by enhancing health awareness and providing reassurance. However, technical issues and integration challenges remain barriers. The developed personas offer HCPs a practical tool to personalize care and identify patients most likely to benefit from wearables.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251375967"},"PeriodicalIF":3.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12515331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145287637","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":"Effect of remote patient monitoring on healthcare use among patients with cancer: A systematic review.","authors":"Callie Rockey-Bartlett, Jennifer Morelli, Melissa Coffel, John Geracitano, Jennifer Elston Lafata, Saif Khairat","doi":"10.1177/20552076251384220","DOIUrl":"https://doi.org/10.1177/20552076251384220","url":null,"abstract":"<p><strong>Purpose: </strong>Remote patient monitoring (RPM) allows healthcare providers to monitor patient outcomes outside of a traditional healthcare setting, potentially supporting reductions in acute care utilization. This systematic review aims to assess whether RPM use among cancer patients reduces hospitalizations and length of stay (LOS).</p><p><strong>Methods: </strong>A systematic review was conducted to identify articles published in PubMed and CINAHL between 2019 and 2024 that evaluated the impacts of RPM in cancer patients compared to the standard care. The primary outcomes examined were hospitalizations and LOS. Secondary outcomes were emergency department (ED) visits and hospital readmissions. Two reviewers screened and assessed the studies. Evidence strength was assessed using the Grading of Recommendations Assessment, Development and Evaluation approach. Risk of bias was assessed using the Newcastle-Ottawa Scale and the Cochrane risk-of-bias tool for randomized trials (RoB 2). While statistical tests were not conducted on results, outcomes were categorized as having increased, decreased, or no change.</p><p><strong>Results: </strong>This review included one randomized controlled trial, seven cohort studies, and one case-control study. RPM was associated with reductions in healthcare utilization among cancer patients. There were significant reductions in hospitalizations, LOS, ED visits, and hospital readmissions in 67% (4/6), 67% (4/6), 75% (3/4), and 67% (2/3) of studies, respectively.</p><p><strong>Conclusion: </strong>This systematic review builds upon existing literature that demonstrates the effectiveness of using RPM to manage acute conditions such as cancer. RPM may support management of various cancer-related conditions and, in turn, potentially reduce acute care use. However, study homogeneity and additional rigorous study designs are necessary to draw more definitive conclusions about the impacts of RPM in cancer care.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251384220"},"PeriodicalIF":3.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12515290/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145287679","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":"Improving tuberculosis-related knowledge in tuberculosis patients: Protocol for the development and validation of an evidence-based Q&A robot powered by large language models.","authors":"Lanping Zhang, Wenjun He, Xiufen Wang, Xue Li, Jinghui Chang, Dong Roman Xu, Guobao Li","doi":"10.1177/20552076251384143","DOIUrl":"https://doi.org/10.1177/20552076251384143","url":null,"abstract":"<p><strong>Background: </strong>Inadequate health knowledge of tuberculosis patients is one of the causes of poor adherence among tuberculosis patients in China's tuberculosis control. In this study, we will develop and validate the effectiveness of a large language model (LLM) to improve the health knowledge of tuberculosis patients.</p><p><strong>Methods: </strong>We will design a LLM application tailored to tuberculosis scenarios and evaluate its effectiveness in tuberculosis patient health education through a single-center, factorial-design randomized controlled trial. The study will feature a factorial design with two factors: LLMs-based health education model and a peer-intervention health education model, each with two levels (yes/no). A total of 148 tuberculosis (TB) patients in the intensive treatment phase will be randomly allocated to four groups through simple randomization. The primary outcome will be the patients' level of personal health knowledge about tuberculosis, measured through questionnaires administered at discharge and three months later.</p><p><strong>Conclusion: </strong>We are the first study in China to apply LLMs to tuberculosis health education. Tailored specifically for TB, our model uses certified guidelines and expert consensus to minimize inaccuracies. Large language models provide access to personalized, private health information, and reducing stigma. Instead of creating a new platform, we use the popular WeChat platform to deliver education via videos, text, and images, enhancing accessibility and engagement. This innovative approach aims to improve patient adherence and contribute to better TB management and disease control outcomes.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251384143"},"PeriodicalIF":3.3,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12511700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281707","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":"Exploring patient satisfaction and its influencing factors in Chinese internet hospitals: An analysis using two-factor theory and Kano model based on user-generated contents.","authors":"Yunfan He, Lei Ye, Xinran He, Jiayi Chen, Tong Wang, Lili Qiao, Hongyu Pu, Yifeng Li, Yujie Wang, Xiaoyi Jiao, Qichuan Fang, Junhao Ma, Mengyao Xing, Yue Hu, Tingting Zhou, Jun Liang, Jianbo Lei, Zhao Star X","doi":"10.1177/20552076251382090","DOIUrl":"https://doi.org/10.1177/20552076251382090","url":null,"abstract":"<p><strong>Objective: </strong>There is currently a lack of in-depth understanding of patient satisfaction and usage of internet hospitals in real-world scenarios. This study aims to comprehensively collect internet hospital Applications (APPs) in China, investigate their patient satisfaction, identify influencing factors, and understand the differences in the factor attributes.</p><p><strong>Methods: </strong>This study was a cross-sectional observational study. We collected China's internet hospital APPs and their patient reviews from eight Chinese APP stores in October 2024. First, data preprocessing was conducted through deduplication, identification of bot accounts, sentiment analysis, and manual inspection. Second, based on the Two-Factor Theory, the Latent Dirichlet Allocation topic model and Tobit model were employed to identify influencing factors. Third, the Wald test was used to examine the effect differences of these factors. Finally, the factor attributes were identified using the Kano model.</p><p><strong>Results: </strong>A total of 148 internet hospital APPs in China and their 121,458 patient reviews were included. The number of these APPs and users showed an initial increase followed by a decrease, peaking in 2020. For influencing factors, 12 factors significantly affected patient satisfaction and dissatisfaction. The Wald test results indicated that there is a significant difference in the influencing effect between patient satisfaction and dissatisfaction. Twelve factors were further categorized into ten charm factors and two essential factors.</p><p><strong>Conclusion: </strong>In recent years, patient satisfaction and real-world usage effectiveness of internet hospital APPs have been suboptimal. Research has shown that influencing factors exhibit asymmetry and can be further classified into charm factors and essential factors. On the one hand, reliability and customer service are basic needs of patients. On the other hand, online diagnosis and treatment functions, doctor's professional level, easy to use, and compatibility can effectively improve patient compliance.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251382090"},"PeriodicalIF":3.3,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12511695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281633","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":"Working women's perceptions and expectations of digital health tools for personal health management: A qualitative study.","authors":"Kiriko Sasayama, Tomoko Saso, Yuko Egawa, Etsuko Nishimura, Erika Ota, Hisateru Tachimori, Ataru Igarashi, Naoko Arata, Daisuke Yoneoka, Eiko Saito","doi":"10.1177/20552076251379747","DOIUrl":"https://doi.org/10.1177/20552076251379747","url":null,"abstract":"<p><strong>Aim: </strong>This study examined the health challenges experienced by women in physically and mentally demanding occupations, including healthcare, shift work, night work and education. It explored their digital health technology use and expectations for future advancements to support their well-being.</p><p><strong>Methods: </strong>In this qualitative study, semi-structured interviews were conducted with 17 full-time working women aged 20 to 64 years, employed in occupations including baking (manufacturing), long-distance truck driving (transportation), cabin crew (aviation), nursing and midwifery (healthcare), teaching (education) and local-level politics. Participants were recruited through purposive sampling. Data were collected via Zoom between June and August 2024. Content analysis identified key themes related to health issues, workplace barriers, use of digital health tools and desired features of future technologies.</p><p><strong>Results: </strong>Participants reported physical and emotional symptoms associated with menstruation and hormonal changes, often worsened by inadequate workplace support. Despite widespread interest in digital health tools such as smartwatches and menstrual tracking apps, adoption was limited due to workplace restrictions, data security concerns, usability challenges and app fatigue. Desired features included simplicity, personalisation and seamless integration into daily routines. Participants emphasised that effective digital health technologies must be accompanied by organisational support and inclusive workplace policies.</p><p><strong>Conclusion: </strong>These findings highlight the urgent need for digital health solutions tailored to the realities of diverse working environments. By incorporating users' lived experiences, particularly from underrepresented occupational sectors, this study offers practical insights into the structural and functional requirements for successful adoption. Collaboration between developers, employers, and policymakers is essential to deliver equitable, secure, and effective digital health support for working women.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251379747"},"PeriodicalIF":3.3,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12511703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281685","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":"Attitudes of mHealth end-users toward integrating mobile technology in HIV program monitoring in Cameroon.","authors":"Frankline Sanyuy Nsai, Palle John Ngunde, Anna Longdoh Njunda, Nicholas Tendongfor, Cho Sabastine Anye, Juliet Nabyonga-Orem, Omer Njajou","doi":"10.1177/20552076251382818","DOIUrl":"10.1177/20552076251382818","url":null,"abstract":"<p><strong>Background: </strong>HIV/AIDS is a major public health issue in Cameroon. Traditional paper-based monitoring systems often fail to provide timely and accurate data, essential for effective program management. The increasing availability of mobile phones and internet services presents an opportunity for mHealth technologies to improve data collection and monitoring. However, the successful integration of mHealth into existing systems requires an understanding of the attitudes and challenges faced by end-users. This study explores the attitudes of mHealth end-users and the challenges they face in integrating mobile technology into HIV program monitoring in Cameroon.</p><p><strong>Methods: </strong>A mixed-methods approach was employed, involving both qualitative and quantitative data collection. The study included 251 respondents from 4 regions (East, Adamawa, Centre, and Littoral) and 4 FGDs. Data were collected using questionnaires and focus group discussion guides. The quantitative data were analyzed using SPSS, while qualitative data were transcribed and analyzed using Nvivo9 with an inductive thematic approach.</p><p><strong>Results: </strong>The quantitative analysis revealed that 78% of respondents had a good attitude toward mHealth, with a mean score of 4.48 ± 0.956. A significant difference was found in the mean attitude scores between those categorized as having good (M = 4.94, SD = 0.244) and poor attitudes (M = 2.71, SD = 0.540; t = -42.50, df = 237, p < 0.001). Qualitative findings supported these results, with most FGD participants expressing a preference for mobile applications over traditional methods due to ease of use and efficiency. Factors such as region of work (F = 5.259, p = 0.002) and educational level (F = 13.45, p < 0.001) significantly influenced attitudes toward mHealth, while age and work experience did not show significant associations. Key challenges identified include the need for better training and support for mHealth technologies, issues with the reliability of mobile devices, and the need for integration with existing systems.</p><p><strong>Conclusion: </strong>There is strong support for the use of mobile technology in improving data collection and patient care. There is a generally positive attitude toward mHealth among end-users in Cameroon, with significant support for its continued integration into HIV program monitoring. However, challenges such as device usability and internet access need to be addressed to enhance the effectiveness of mHealth interventions. Future initiatives should consider these factors to improve data collection and program monitoring in Cameroon.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251382818"},"PeriodicalIF":3.3,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12508549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281649","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":"Self-administered lexical retrieval therapy app in post-stroke aphasia: A pilot study evaluating feasibility, usability, and effectiveness.","authors":"SaeByeol Kim, Minjung Kim, Sooyoun Cho, Jungwan Kim, Jinwoo Kim, Yuyoung Kim","doi":"10.1177/20552076251381630","DOIUrl":"https://doi.org/10.1177/20552076251381630","url":null,"abstract":"<p><strong>Background: </strong>Post-stroke aphasia impairs language abilities. Although digital therapy apps offer accessible self-administered alternatives, the effectiveness of effortful lexical retrieval in such therapies remains unclear.</p><p><strong>Objective: </strong>This study aimed to evaluate the feasibility, usability, and preliminary effectiveness of a tablet-based lexical retrieval therapy app designed to support effortful lexical retrieval in individuals with post-stroke aphasia.</p><p><strong>Methods: </strong>Patients with post-stroke aphasia were randomly assigned to receive lexical retrieval therapy app or workbook-based cognitive exercises for two weeks. Feasibility was assessed through adherence and engagement and usability through the system usability scale (SUS). Preliminary effectiveness of the proposed method was measured by improvements in retrieval performance and the Boston Naming Test (BNT).</p><p><strong>Results: </strong>The study included 17 participants, with the intervention group (9/17) showing an 88.9% retention rate. They showed significantly higher post-intervention lexical retrieval scores than the control group (F_1,14 = 10.82, p = .005). BNT scores significantly improved in the intervention group compared to the control group (<i>F</i> _1,14 = 12.94, <i>p</i> = .003). Intrinsic motivation scores were significantly higher in the intervention group (p = .002), and usability was rated as excellent (mean = 84.56). Training accuracy improved over time, and response time data indicated increased retrieval effort in some participants.</p><p><strong>Conclusions: </strong>This study provides preliminary evidence that a self-administered app using effortful therapy can improve lexical retrieval in post-stroke aphasia, highlighting its potential as an accessible alternative to clinician-led interventions. Future research should explore long-term adherence, adaptive strategies, and their lasting therapeutic effects.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251381630"},"PeriodicalIF":3.3,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12508555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281709","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}