DIGITAL HEALTH最新文献

{"title":"Experiences of an internet-based rehabilitation program: An interview study among individuals with chronic whiplash-associated disorders.","authors":"Anna Hermansen, Gunnel Peterson, Anneli Peolsson, Emma Nilsing-Strid","doi":"10.1177/20552076261483535","DOIUrl":"10.1177/20552076261483535","url":null,"abstract":"<p><strong>Introduction: </strong>Given the rapid development of digital healthcare interventions, incorporating patients' perspectives is essential. However, experiences of internet-based exercise programs for chronic whiplash-associated disorders (WAD) remain unexplored. The aim of this study was to explore and describe experiences of an internet-based rehabilitation program combined with four physiotherapy visits for individuals with chronic WAD from a quality-of-care perspective.</p><p><strong>Methods: </strong>This study employed a qualitative explorative and descriptive study design. Individual semi-structured interviews were conducted with 13 individuals with chronic WAD who had participated in an internet-based rehabilitation program combined with four physiotherapy visits. Data were analysed using a deductive qualitative content analysis based on Donabedian's model of quality of care (structure, process, and outcome).</p><p><strong>Results: </strong>The analysis resulted in three deductively-developed categories: appraisal of content and technical structure of the internet-supported program (structure); patient actions during the rehabilitation process with or without interaction with the internet-supported program (process); and describing valuable outcomes from the patient's perspective (outcomes). Each category was supported by 3-5 sub-categories.</p><p><strong>Conclusions: </strong>Individuals with chronic WAD participating in a neck exercise program with digital support experienced positive health outcomes. The digital format was appreciated, but a more user-friendly format would have been preferred. Instead, the informants relied more on the physiotherapists' instructions, and the digital support served as a good complement to the visits at the physiotherapy clinic. Further knowledge is needed regarding how to combine digital and face-to-face visits and how to make digital interventions in healthcare more user-friendly for both patients and healthcare providers.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261483535"},"PeriodicalIF":3.9,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921741","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":"A methodological study of clustering evaluation in medical data: Exploring internal and stability-based criteria.","authors":"Azam Orooji, Farzaneh Kermani, Seyed Mohsen Hosseini, Alireza Borhani","doi":"10.1177/20552076261486967","DOIUrl":"10.1177/20552076261486967","url":null,"abstract":"<p><strong>Background: </strong>With the increasing volume and complexity of medical data, clustering methods have become valuable tools for discovering hidden patterns in unlabeled datasets.</p><p><strong>Objective: </strong>This study aims to compare the performance of different clustering algorithms for analyzing medical datasets, and evaluate the effectiveness of different clustering quality criteria, including internal, stability and external metrics.</p><p><strong>Methods: </strong>A descriptive analysis was conducted on five publicly available medical datasets: Indian Liver Patient Disease (ILPD), Breast Cancer, Pima Indians Diabetes, Statlog (Heart) and Polycystic Ovary Syndrome (PCOS). After preprocessing (handling missing values, normalization, and variable encoding), eight clustering algorithms-Hierarchical Clustering (HC), k-means clustering, Fuzzy ANalYsis (FANNY), Self-Organizing Tree Algorithm (SOTA), Divisive Analysis (DIANA), Partitioning Around Medoids (PAM), Clustering Large Applications (CLARA) and AGglomerative NESting (AGNES) were applied. Clustering quality was evaluated using internal measures (Connectivity, Silhouette Width, Dunn Index), stability measures (Average Proportion of Non-overlap (APN), Average Distance (AD), Average Distance between Means (ADM) and Figure of Merit (FOM)) and external measures (Accuracy and Adjusted Rand Index (ARI)).</p><p><strong>Results: </strong>HC and AGNES consistently achieved the best internal and stability performance on the ILPD, Pima, and Statlog datasets, while k-means performed best on the Breast Cancer dataset. On the PCOS dataset, HC, AGNES, k-means, and DIANA showed comparable performance. However, external validation revealed that higher internal and stability scores did not necessarily correspond to greater agreement with clinical class labels, and the optimal algorithm varied across datasets.</p><p><strong>Conclusion: </strong>No clustering algorithm consistently outperformed the others across all medical datasets. Although hierarchical methods showed strong internal and stability performance, these metrics alone were insufficient to predict agreement with clinical labels. Therefore, external validation should complement internal and stability metrics to provide a more comprehensive evaluation of clustering performance in medical data analysis.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261486967"},"PeriodicalIF":3.9,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921717","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 InterRAI care processes in long-term care homes: A constructivist grounded theory.","authors":"Steve Iduye, Caitlin McArthur, Khaldoun Aldiabat, Jill Bally, Tracie Risling","doi":"10.1177/20552076261486775","DOIUrl":"10.1177/20552076261486775","url":null,"abstract":"<p><strong>Objective: </strong>To provide an in-depth understanding of the care processes involving the use of the Long-Term Care Facilities (LTCF) tool in Nova Scotia, Canada. Specifically, this constructivist grounded theory study explored the perspectives of long-term care (LTC) residents, substitute decision-makers (SDMs), and nurses, with the aim of improving residents' care.</p><p><strong>Methods: </strong>A constructivist grounded theory study was conducted in LTC homes in Nova Scotia, Canada, between September 2022 and April 2023. Data were collected through 38 semi-structured interviews with residents, SDMs, nurses, supplemented by 41 field notes and 10 analytical memos. Data were analyzed using constructivist grounded theory methods.<b>Results:</b>The emerging substantive theory was developed primarily from the perspectives of nurses and LTC management, while residents' and SDMs' perspectives provided contextual and supportive insights. The participants described InterRAI LTCF care process improvement through relationship-centredness, intentionality, and effective change management. If implemented properly at the point of care, these strategies could facilitate consistent care improvement for LTC residents.</p><p><strong>Conclusions: </strong>The findings have important implications for practice, policy, and future research. Care process improvement in LTC settings requires attention to relational, organizational, and technological factors. These implications are particularly relevant for LTC management, point-of-care nurses, and LTCF software designers seeking to enhance the quality and consistency of residents' care.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261486775"},"PeriodicalIF":3.9,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921788","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":"Mapping the landscape of MRI research in post-stroke cognitive impairment: A bibliometric analysis from 2000 to 2026.","authors":"Lihui Yan, Siyu Gu, Xiaolu Xu, Hengheng Liu, Ningjie Yu, Yuan Shen, Hualiang Li, Wenqi Wang, Xianxian Zhang, Fei Chen, Zhiping Zhang","doi":"10.1177/20552076261487046","DOIUrl":"10.1177/20552076261487046","url":null,"abstract":"<p><strong>Objective: </strong>Magnetic Resonance Imaging (MRI) is essential for understanding post-stroke cognitive impairment (PSCI), yet the global research landscape remains unclear. This study aimed to analyze the trends and hotspots in MRI research on PSCI through bibliometric analysis.</p><p><strong>Methods: </strong>Publications were retrieved from the Web of Science Core Collection on June 2, 2026, covering January 1, 2000 to June 2, 2026. Only English-language records classified as \"Article\" were included. Bibliometric indicators, collaboration networks, and keyword dynamics were analyzed using VOSviewer (version 1.6.20), CiteSpace (version 6.4.R1), and the R package bibliometrix (version 4.5.3).</p><p><strong>Results: </strong>A total of 2,022 articles were included. The USA (438 corresponding-author articles) and China (422) were the leading contributors. The USA had the highest total citations (25,505), while average citation impact was higher in the UK and the Netherlands than in China. The University of California System (314 publications), Boston University (295), and Harvard University (272) were the most productive institutions. <i>Stroke</i> published the most articles (171) and showed the strongest network prominence in journal analyses. Keyword co-occurrence revealed five major thematic clusters spanning vascular risk and cerebral small vessel disease markers, cognitive phenotype and assessment, and network-oriented neuroimaging. Burst detection indicated current research frontiers through 2026 centered on \"white matter hyperintensities,\" \"cerebral small vessel disease,\" \"functional connectivity,\" \"connectivity,\" and \"recovery.\"</p><p><strong>Conclusion: </strong>MRI research on PSCI expanded substantially over the study period, while thematic emphasis shifted from mainly structural and vascular lesion markers toward network-based and prognosis-oriented imaging. Future progress will require stronger international collaboration, more standardized MRI and analytic workflows, and external validation of multimodal prediction models in well-phenotyped cohorts before clinical translation.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261487046"},"PeriodicalIF":3.9,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13542547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148898437","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":"Telehealth interventions for patients with noncommunicable diseases in Africa: A scoping review protocol.","authors":"Anthea Rhoda, Maphoko Malema, Nondwe Mlenzana, Lucian Bezuidenhout, David Conradsson, Conran Joseph, O A Olotu, José Frantz","doi":"10.1177/20552076261434065","DOIUrl":"10.1177/20552076261434065","url":null,"abstract":"<p><strong>Background: </strong>Telehealth is a system that makes use of information and technology communication to ensure that patients get access to healthcare services regardless of the distance and shortage of healthcare professionals. Telehealth is a strategy that can be applied to complement the management of noncommunicable diseases (NCDs) in Africa. This review therefore aims to: (1) collate the available data about the process and outcomes of telehealth programs implemented in Africa and (2) explore the challenges experienced with the implementation of telehealth in the African context.</p><p><strong>Methods: </strong>The scoping review will summarize the information by accessing the following databases; Ebscohost (CINAHL + MEDLINE, PsychINFO, Web of Science and PubMED), Scopus and google scholar. The databases will be searched using specific search terms that are aligned with the aim of the review. Studies will be included if they investigated the telehealth interventions for NCDs in Africa. A screening process that includes screening of the abstracts and full text will identify the studies to be included. A data capture template designed specifically for the study will be used to capture data. The findings will be presented descriptively and thematically.</p><p><strong>Conclusion: </strong>This study underscores the increasing opportunity that exists for telehealth interventions to address NCDs, in resource-constrained environments. The intended review could confirm the potential of digital health to increase access to care, promote patient self-management, and engage health systems where resources are lacking.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261434065"},"PeriodicalIF":3.9,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148901594","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":"The relationship between prognostic nutritional index and in-hospital mortality in patients with severe coronary heart disease: A multicenter retrospective cohort study and predictive model based on machine learning.","authors":"Caiping Tang, Yulai Wu, Lin Xie, Ziyu Lin, Lihua Wang, Zhenyi Yan, Xiaoyan Liu, Xijian Liang, Zhiyi Li","doi":"10.1177/20552076261486988","DOIUrl":"10.1177/20552076261486988","url":null,"abstract":"<p><strong>Background: </strong>The prognostic value of the Prognostic Nutritional Index (PNI) in critically ill patients with coronary heart disease (CHD) remains unclear. This multicenter retrospective cohort study aimed to investigate the association between PNI and in-hospital mortality in severe CHD patients and develop a machine learning-based predictive model.</p><p><strong>Methods: </strong>This study is a multicenter retrospective cohort study, extracting data of adult critically ill patients with CHD who met the inclusion criteria from the MIMIC-IV database and the eICU-CRD database. Multivariate logistic regression and restricted cubic spline (RCS) analyses were used to evaluate the relationship between PNI and in-hospital mortality and 365-day mortality. The Boruta algorithm and LASSO regression were used to screen predictive features, six machine learning models were constructed, and SHapley Additive explanation (SHAP) was used to interpret the importance of model features.</p><p><strong>Results: </strong>Low PNI was significantly associated with higher in-hospital mortality (OR=0.95, 95% CI 0.94-0.97). RCS revealed a linear negative relationship between PNI and mortality. The Random Forest model demonstrated superior predictive performance (AUC=0.846), significantly outperforming traditional scoring systems. SHAP analysis identified CRRT, Sepsis, Vasopressor, PNI, and BUN as key predictors. An online clinical calculator was developed based on these findings.</p><p><strong>Conclusions: </strong>This study developed a machine learning-based calculator to predict in-hospital mortality in patients with CHD in the ICU (https://leongxj.shinyapps.io/chd-mortality-prediction/). This calculator may guide clinical decision-making and improve the treatment of patients with CHD by identifying those at higher risk of in-hospital death.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261486988"},"PeriodicalIF":3.9,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921764","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":"Open-source versus proprietary large language models for academic writing in medicine: A controlled evaluation of DeepSeek-R1 and ChatGPT-o1 in neuroradiology.","authors":"Semil Eminovic, Bogdan Levita, Robin Schmidt, Maximilian Lindholz, Dmitriy Desser, Tobias Penzkofer, Mike P Wattjes, Jawed Nawabi","doi":"10.1177/20552076261464745","DOIUrl":"10.1177/20552076261464745","url":null,"abstract":"<p><strong>Objectives: </strong>Large language models (LLMs) are being used to facilitate academic writing. We aimed (1) to assess the quality, integrity, and factual reliability of LLM-assisted academic writing in medicine, and (2) to compare the performance of open-source versus proprietary LLMs, reflecting differences in model transparency.</p><p><strong>Methods: </strong>In this prospective, controlled evaluation study, an open-source model (DeepSeek-R1) and proprietary model (ChatGPT-o1) completed ten academic tasks: generating five scientific essays and five evidence-based question-answers covering clinical topics in neuroradiology. Two radiologists rated outputs using 14 Likert and 7 binary criteria across the domains of academic quality, linguistic expression, factual reliability. Paired Wilcoxon/McNemar (Holm) assessed differences; ICC/Cohen's κ assessed reliability.</p><p><strong>Results: </strong>DeepSeek-R1 achieved higher overall mean Likert scores than ChatGPT-o1 (mean ratings ± standard deviation: 3.23 ± 0.44 vs 3.02 ± 0.30, p = 0.021). It significantly outperformed in reasoning depth (p = 0.015), contextual coherence (p = 0.043), subtlety (p = 0.037), and evidence integration (p = 0.027). Although both models cited sources in every output, citation reliability was poor. DeepSeek-R1 cited more real publications (55.1% vs. 33.3%, p=0.053) but showed a higher confirmed fabrication rate (36.7% vs. 2.6%, p<0.001). DeepSeek-R1 respected word limits in 90%, ChatGPT-o1 only in 50%. Neither reviewer reliably noticed AI-generated text features. Inter-rater reliability was poor for Likert criteria (ICC = 0.466) and substantial for binary items (κ = 0.746).</p><p><strong>Conclusion: </strong>DeepSeek-R1 outperformed ChatGPT-o1 in generating academic content for neuroradiology, highlighting the potential of transparent, locally deployable models. However, moderate quality, citation errors and hallucinations indicate that LLMs are not yet sufficient for unsupervised academic writing and demand rigorous human review.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261464745"},"PeriodicalIF":3.9,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889309","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":"The use of the unified theory of acceptance of technology model for telemedicine: A systematic review for developing a research agenda for future studies.","authors":"Mateus de Sousa Martins, Bento Alves da Costa Filho, Maria Fátima de Sousa, Ana Valéria Machado Mendonça, Natália Fernandes de Andrade, Silvia Régia Vieira de Freitas Filha, Luana Dias da Costa, João Paulo Fernandes da Silva, Julys Nathan Ferreira Soares, Igor Bettarello Carvalho Xavier, Josivania Silva Farias","doi":"10.1177/20552076261435081","DOIUrl":"10.1177/20552076261435081","url":null,"abstract":"<p><strong>Background: </strong>Digital health technologies improve access to healthcare and offer innovative ways to assist its users, as health systems increasingly integrate telemedicine strategies into medical service routines. Understanding the factors that influence the acceptance and use of telemedicine-related technologies is fundamental. The unified theory of acceptance of technology (UTAUT) is one of the most frequently applied theoretical frameworks for explaining technology adoption and has evolved since its development. Mapping the literature on the application of UTAUT in telemedicine studies is key to understanding the future of research in this area.</p><p><strong>Objective: </strong>The main objective of this study is to map the literature on the application of the UTAUT model for telemedicine acceptance and use, in order to assist in developing a framework for future research agendas.</p><p><strong>Methods: </strong>A systematic literature review of the application of the UTAUT model to telemedicine acceptance and use was conducted, following the PRISMA protocol. The textual corpus obtained from the collected data was analyzed, with the support of VOSviewer and IRAMUTEQ software, to assist in developing a framework for future research agendas in telemedicine.</p><p><strong>Results: </strong>The current literature on the application of the UTAUT model to the acceptance and use of telemedicine was mapped, analyzed, and applied to develop a research agenda framework that provides guidance for understanding and structuring research directions.</p><p><strong>Conclusion: </strong>This study maps UTAUT's application in telemedicine and identifies key factors shaping technology acceptance and use. It advances theoretical understanding, offers a framework for future research, and supports the integration of digital health technologies to improve healthcare delivery.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261435081"},"PeriodicalIF":3.9,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148901566","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 mHealth tool to prevent chronic childhood malnutrition in Ecuador: A user-centered design approach in developing contexts.","authors":"María Pilar Villena-Esponera, Harry Aguilar Gamarra, Carlos Jerves-Córdova, Guillermo Molina-Recio, Marta Caballero","doi":"10.1177/20552076261460352","DOIUrl":"10.1177/20552076261460352","url":null,"abstract":"<p><strong>Background: </strong>Chronic malnutrition in children (CMC) remains a pressing public health issue in Ecuador, especially in the rural Sierra communities. Support from community health workers (CHWs) to families during the first 1000 days of life is crucial for prevention. However, CHWs face operational challenges that reduce intervention effectiveness, emphasising the need to develop mobile health (mHealth) tools that are contextually appropriate, culturally relevant, and technically reliable.</p><p><strong>Objective: </strong>To design an mHealth tool (called Mikuy) that enables CHWs to implement a family support strategy to prevent CMC in rural Ecuador.</p><p><strong>Methods: </strong>A user-centered design tailored to the local context was employed, using a mixed participatory methodology involving 99 participants (84 pregnant women or mothers and 15 CHWs). The process unfolded in three iterative stages: (1) analysis of context and needs, (2) design of content and interface, and (3) usability and acceptability assessment. Qualitative techniques included focus groups, a co-design workshop, one-to-one think-aloud sessions, and observations during simulated use. Quantitative methods involved administering usability (SUS) and acceptability (TAM) questionnaires.</p><p><strong>Results: </strong>Participants identified critical needs relating to connectivity and cultural appropriateness. Key features for effectiveness included offline accessibility, language switching (Spanish/Kichwa), household georeferencing, and a system for monitoring home visits. Qualitative observations led to improvements in interface, navigation, and language. The final tool scored highly in usability tests (SUS = 83.75) and acceptability (TAM-PU ≥ 4.60).</p><p><strong>Conclusions: </strong>Mikuy is a culturally adapted and operationally viable mHealth solution that supports CHWs and other community agents in educational and monitoring efforts to prevent CMC in rural areas of the Ecuadorian Sierra.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261460352"},"PeriodicalIF":3.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13535078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882566","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":"Flexible pressure sensor pants for app-supported sleep posture monitoring: Feasibility, accuracy, and user experience study.","authors":"Yongen Miao, Jinchuan He, Ji Chen, Yingchuan Zhao, Yunshan Fan, Sheng Yang, Shisheng He","doi":"10.1177/20552076261470647","DOIUrl":"10.1177/20552076261470647","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to develop and validate an innovative wearable system-flexible pressure sensor pants-to overcome the privacy, comfort, and portability limitations of conventional sleep posture monitoring methods (e.g., infrared imaging and pressure-sensitive mattresses). The study evaluates the system's posture classification performance and its feasibility for continuous monitoring in real-world settings.</p><p><strong>Methods: </strong>Thirty-four young adults were recruited. During short-term wakefulness trials, participants wore the sensor-integrated sleep pants and assumed four predefined postures (supine, prone, left lateral, and right lateral), yielding 202 annotated posture instances. A confusion matrix was employed to derive sensitivity, specificity, and overall accuracy. Overnight monitoring involved concurrent data acquisition from the sensorized sleep pants and an infrared camera. Statistical analyses included paired t-tests or Mann-Whitney U tests to examine inter-method differences, Bland-Altman plots for agreement assessment, and scatter plots to quantify linear correlations. User feedback on comfort and impact on sleep was collected via questionnaire.</p><p><strong>Results: </strong>In awake trials, the system achieved 96% overall accuracy, with mean sensitivity and specificity of 0.95 (0.93-0.98) and 0.98 (0.97-0.99), respectively. Overnight data revealed no statistically significant differences between the two monitoring methods across all postures. Bland-Altman analyses indicated high concordance, while scatter plots demonstrated strong correlations: prone (R=0.5423, <i>p<</i>.001), supine (R=0.8365, <i>p<</i>.001), right lateral (R=0.9108, <i>p<</i>.001), and left lateral (R=0.4665, <i>p<</i>.001). User satisfaction reached 94.1%.</p><p><strong>Conclusion: </strong>Flexible pressure sensor pants demonstrated high accuracy, strong agreement with infrared monitoring. The high user satisfaction and practical convenience indicate their potential to meet essential requirements for sleep posture assessment. Compared with traditional methods, they offer greater comfort, portability, and privacy protection, supporting feasibility for real-world sleep posture monitoring.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261470647"},"PeriodicalIF":3.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13535072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882471","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}