DIGITAL HEALTH最新文献

{"title":"Digital health nursing in male infertility: Leadership, workforce, and policy pathways for equitable care.","authors":"Mehrdad Abdullahzadeh","doi":"10.1177/20552076261477990","DOIUrl":"10.1177/20552076261477990","url":null,"abstract":"<p><p>Digital health technologies are reshaping reproductive care, yet their application to male infertility-an area marked by stigma, delayed help-seeking, and structural invisibility-remains underdeveloped. This narrative review synthesizes literature from 2018-2025 to examine how tele-nursing, mobile health, artificial intelligence, and digitally mediated peer support can strengthen access, privacy, and engagement for men experiencing infertility. A structured search of PubMed, CINAHL, Scopus, and Google Scholar identified 67 relevant sources, which were synthesized across four domains: digital innovation, masculinity and stigma, workforce readiness, and governance gaps. Findings show that digital health offers meaningful opportunities to reduce barriers to care but also carries risks-including algorithmic bias, misinformation, and unequal digital access-that may reinforce existing inequities without intentional oversight. To address these tensions, the Nursing Leadership Mediation Model is proposed, integrating sociotechnical, workforce, and policy perspectives to position nurse leaders as mediators who align digital innovation with ethical, equitable implementation. Advancing digital health in male infertility care will require strengthened digital competencies, culturally responsive practice, and explicit policy inclusion to ensure that emerging technologies support-not marginalize-men's reproductive health needs.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261477990"},"PeriodicalIF":3.9,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554684/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921727","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":"Artificial intelligence applications in knee osteoarthritis research: A bibliometric and visualized analysis.","authors":"Muyun Yang, Jie He, Ruoyu Zhuang, Jin Wang, Laijun Yan, Hao Zhang, Yuelong Cao","doi":"10.1177/20552076261486794","DOIUrl":"10.1177/20552076261486794","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) has become a transformative tool in knee osteoarthritis (KOA) research, providing new opportunities for diagnosis, prognosis, disease monitoring, and personalized management. However, the development trajectory, collaboration patterns, and emerging hotspots of this field remain insufficiently mapped. This study aimed to provide a bibliometric and visualized analysis of AI applications in KOA research.</p><p><strong>Methods: </strong>Publications on AI applications in KOA from 2004-October 2025 were retrieved from the Web of Science Core Collection. Bibliometric indicators and collaboration networks, intellectual structure, and thematic evolution were analyzed using Bibliometrix, VOSviewer, CiteSpace, and Bibliometric.com.</p><p><strong>Results: </strong>A total of 663 publications were included in the final analysis, comprising 582 articles, 2 early access articles, 3 proceedings papers, and 76 reviews. The annual growth rate was 28.03%, indicating rapid expansion of the field. China led in publication output, while the United States ranked first in total citations and average citations. International collaboration exhibited a tripolar structure dominated by North America, Europe, and Asia, although cross-regional integration remained limited. <i>Osteoarthritis and Cartilage</i> was the most productive and influential journal. Thematic evolution revealed a shift from algorithm-centered methodological development, including deep learning and radiomics, toward clinically oriented applications such as predictive modeling, imaging-based assessment, arthroplasty, and decision support.</p><p><strong>Conclusion: </strong>AI-related KOA research has grown rapidly, evolving from methodological exploration toward clinical translation. Future studies should strengthen international collaboration, multicenter validation, and integration of AI into real-world KOA management.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261486794"},"PeriodicalIF":3.9,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921663","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":"Dynamic alignment of large language models for evidence-grounded heart failure decision support.","authors":"Lu Liu, Chenchen Dong, Yunbo Ba, Haihong Yan, Xiaoxiao Tang, Yu Sun, Huilin Chen, Boyuan Shi, Qin Yu, Shulong Zhang","doi":"10.1177/20552076261487568","DOIUrl":"10.1177/20552076261487568","url":null,"abstract":"<p><strong>Objectives: </strong>Large language models (LLMs) are increasingly studied for clinical decision support, but high-risk cardiology exposes persistent weaknesses in hallucination control, guideline adherence, and medication-safety reasoning. Heart failure with reduced ejection fraction (HFrEF) is a demanding test case because safe care requires structured guideline-directed therapy, comorbidity-aware monitoring, and reliable risk warnings.</p><p><strong>Methods: </strong>We developed a dynamic alignment framework using 1087 retrospective HFrEF cases from Affiliated Zhongshan Hospital of Dalian University. An open-source LLaMA-3.1 backbone was optimized through four sequential stages: continual pre-training for heart-failure domain adaptation, supervised fine-tuning for structured clinical responses, reinforcement policy optimization for safety-oriented alignment, and retrieval-augmented generation for guideline grounding. Models were assessed with dual-track clinical and linguistic metrics.</p><p><strong>Results: </strong>LLaMA-3.1 was the strongest supervised baseline, but supervised fine-tuning alone did not fully resolve guideline-adherence limitations. Staged alignment produced a measurable Alignment Tax: the final retrieval-grounded variant improved the Clinical Score from 0.716 to 0.864 and reached a Guideline Score of 0.881, while BLEU-4 decreased from 0.371 to 0.272. The decline in surface overlap coincided with stronger risk safety, stricter structure, and more guideline-directed outputs.</p><p><strong>Conclusions: </strong>Dynamic alignment shifted the model from linguistic mimicry toward clinically constrained HFrEF decision support. These findings suggest that staged optimization with policy alignment and retrieval grounding can improve evidence-based recommendations, while conventional language-overlap metrics may underestimate clinically safer generation.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261487568"},"PeriodicalIF":3.9,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921676","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":"Objectively measured movement behaviors of adult transtibial prosthesis users.","authors":"Kyle R Leister, Tiago V Barreira","doi":"10.1177/20552076261486359","DOIUrl":"10.1177/20552076261486359","url":null,"abstract":"<p><strong>Objective: </strong>Understanding real-world movement behavior is essential for optimizing rehabilitation for people with lower limb amputation, yet clinical measures reflect performance mainly in controlled settings. Free-living studies are often small, and individuals with transtibial amputation generally demonstrate reduced physical activity and high sedentary time relative to population norms. Clinicians lack robust reference values that reflect daily-life behavior. Therefore, the aim of this study was to characterize 24-hour (h) movement behavior patterns in a larger cohort than typically reported and establish percentile-based reference values to support individualized rehabilitation.</p><p><strong>Methods: </strong>A cross-sectional, observational design using objective monitoring of free-living movement behaviors was implemented. Ninety-six adults with unilateral transtibial amputation were recruited from prosthetic clinics across the United States (mean age 58±13.5 years; 24 female). Participants self-reported their age, sex, ethnicity, diabetes status, amputation etiology, years of prosthesis use, and the age of their prosthesis. Height and weight were measured, and amputation-adjusted BMI was calculated. Participants wore activPAL and Fitbit devices for seven days to quantify steps, sedentary time, prolonged sedentary bouts (>1 h), standing time, stepping time, sleep duration, and sit-to-stand transitions. Percentile distributions summarized movement behaviors, and time-use compositions described daily allocation of sedentary, standing, stepping, and sleep time.</p><p><strong>Results: </strong>Participants averaged 4,712 steps/day with activPAL and 5,815 steps/day with Fitbit. Median sedentary time was 616 minutes (min) (10.26 h)/day, with mean sleep duration of 500 min (8.33 h)/night. At the extremes, the 5th percentile accumulated 701 steps/day with 386 min (6.43 h) of sedentary behavior, whereas the 95th percentile accumulated 10,691 steps/day and 905 min (15.08 h) of sedentary behavior. Stepping time ranged from 11 to 164 min (2.73 h)/day. Significant differences (p<0.006) were observed by functional mobility classification, obesity status, and diabetes status.</p><p><strong>Conclusion: </strong>Free-living movement behavior showed substantial variability and pervasive sedentary time. Percentile-based benchmarks provide clinically useful context for assessing mobility and informing personalized rehabilitation targets.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261486359"},"PeriodicalIF":3.9,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921785","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":"Exploratory task-specific evaluation of large language models in lung cancer clinical scenarios: A comparative study.","authors":"Wenzheng Zhang, Xue Li, Run Yuan, Yinuo Zhang, Yufei Yang, Yun Xu, Ruikang Zhong, Siyi Chen, Dianna Liu, Lei Gao, Kaiwen Hu","doi":"10.1177/20552076261487079","DOIUrl":"10.1177/20552076261487079","url":null,"abstract":"<p><strong>Background: </strong>Lung cancer remains the leading cause of cancer-related mortality worldwide. Large language models (LLMs), including ChatGPT, DeepSeek, and Grok, have shown promise in clinical decision support, but differences in training and alignment may lead to variable performance. Current evaluations often rely on aggregate metrics or isolated tasks, which may not capture real-world clinical complexity.</p><p><strong>Methods: </strong>We conducted a structured evaluation of three LLMs using nine simulated lung cancer cases across five clinical domains. LLMs' outputs were anonymized, randomized, and independently scored by five senior lung cancer specialists under a double-blind design using a five-point Likert scale evaluating accuracy, comprehensiveness, relevance, and clinical applicability. Qualitative error analysis was also performed.</p><p><strong>Results: </strong>Inter-rater agreement was moderate (Fleiss' κ = 0.463; ICC (2, k) = 0.675). All LLMs achieved high scores across evaluation dimensions without statistically significant differences (P > 0.05). Given the limited number of simulated cases, these findings should be interpreted cautiously. Descriptive analyses suggested context-dependent performance patterns across clinical domains: Grok tended to show more consistent performance in diagnosis and treatment decision-making, DeepSeek showed comparatively lower descriptive performance in therapeutic decisions but higher applicability in prognosis and rehabilitation, and GPT exhibited relatively stable intermediate performance. No single LLM consistently outperformed others across all clinical scenarios.</p><p><strong>Conclusion: </strong>LLMs demonstrate substantial potential in supporting lung cancer clinical workflows, but their performance appears to be context-dependent. The present findings are exploratory and suggest that task-specific evaluation may provide a more clinically informative framework than overall model ranking. Continued validation using larger and more diverse clinical datasets, together with appropriate governance and specialist oversight, remains essential for the safe integration of LLMs into clinical practice.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261487079"},"PeriodicalIF":3.9,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921749","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 two-stage evaluation of large language models for guideline-based Q&A and draft rehabilitation planning in stroke care.","authors":"Yi-Lin Wang, Liao-Lin Chen, Ping-Ping Sun, Zhen-Zhen Ma, Ye-Ping Chen, Yang-Shuo Ge, Xin-Hui Huang, Chun-Meng Huang, Jia-Wei Du, Ting-Ting Meng, Dao-Fang Ding","doi":"10.1177/20552076261484840","DOIUrl":"10.1177/20552076261484840","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate large language models (LLMs) for stroke care in guideline-based question answering (Q&A) and individualized draft rehabilitation-plan generation, with explicit assessment of response quality, readability, and repeated-generation score stability.</p><p><strong>Methods: </strong>A two-stage study was conducted. Stage 1: Four LLMs generated best answers to 100 stroke-related questions; the top two models by accuracy advanced to Stage 2. In Stage 2a, ChatGPT-o3 and DeepSeek answered 20 guideline-derived clinical questions, each repeated three times. In Stage 2b, the two models generated individualized rehabilitation plans for 60 de-identified stroke cases, with three independent generations per case. Three senior clinicians evaluated outputs across correctness, completeness, readability, helpfulness, and safety using 5-point Likert scales. Chinese readability was assessed using the LDU-TGP platform, and English readability was assessed using the Flesch-Kincaid Grade Level. Generalized estimating equations were used for Stage 2a and Stage 2b comparisons to account for repeated generations clustered within clinical questions and patient cases, respectively.</p><p><strong>Results: </strong>In Stage 1, DeepSeek achieved the highest accuracy (91%), followed by ChatGPT-o3 (90%), Gemini (85%), and ChatGPT-4o (83%). In Stage 2a, no between-model differences across the five clinician-rated domains remained statistically significant after correction for multiple comparisons. ChatGPT-o3 generated clinical Q&A responses with a higher recommended reading age and English Flesch-Kincaid Grade Level, whereas the difference in Chinese Reading Difficulty Score did not remain significant after correction. In Stage 2b, ChatGPT-o3 significantly outperformed DeepSeek across correctness, completeness, readability, helpfulness, and safety in the GEE analysis. Repeated-generation score stability did not differ significantly between models.</p><p><strong>Conclusions: </strong>Within this single-center, expert-rated benchmark, ChatGPT-o3 and DeepSeek performed comparably across most guideline-based Q&A dimensions, whereas ChatGPT-o3 achieved higher clinician-rated scores for draft rehabilitation-plan generation. These findings do not establish clinical effectiveness. Prospective patient-centered validation and clinician-supervised review are required before implementation in routine stroke rehabilitation.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261484840"},"PeriodicalIF":3.9,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148901526","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":"How incidental exposure shapes continued use of AI chatbots in healthcare: A multigroup analysis.","authors":"Shuming Yang, Zikun Liu, Yangwen Jiang","doi":"10.1177/20552076261487241","DOIUrl":"10.1177/20552076261487241","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to examine the linkage between incidental information exposure and individuals' continuous intention to use AI chatbots in healthcare, and whether this process varies by pre-existing attitudes toward human doctors.</p><p><strong>Method: </strong>Data were collected through a cross-sectional survey (<i>N</i> = 779) from June 11 to June 16, 2025. Structural equation modeling and multigroup comparison analysis were conducted to test the hypothesized relationships and group differences.</p><p><strong>Result: </strong>Incidental exposure to information about AI chatbots in healthcare was positively linked to both subjective (<i>β</i> = .378, <i>p</i> < .001) and objective knowledge (<i>β</i> = .095, <i>p</i> < .05). Both subjective (<i>β</i> = .222, <i>p</i> < .001) and objective knowledge (<i>β</i> = .224, <i>p</i> < .001) about AI chatbots in healthcare were positively linked to positive attitudes toward these technologies, which was subsequently associated with continuous intention (<i>β</i> = .468, <i>p</i> < .001). Multigroup analysis showed that the link between objective knowledge and positive attitudes toward AI chatbots in healthcare was stronger among those with high (<i>β</i> = .299, <i>p</i> < .001) versus low (<i>β</i> = .135, <i>p</i> = .014) positive attitudes toward human doctors, as was the link between positive attitudes and continuous intention (high: <i>β</i> = .506; low: <i>β</i> = .271, <i>p</i>s < .001).</p><p><strong>Conclusion: </strong>Incidental exposure to media information about AI chatbots in healthcare was positively linked to continuous usage intention through knowledge acquisition and attitude formation. Individuals with divergent levels of pre-existing attitudes toward human doctors performed differently within the mechanism.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261487241"},"PeriodicalIF":3.9,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148901530","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 effects of virtual reality interventions on depression and anxiety in older adults during and after the COVID-19 pandemic: A meta-analysis.","authors":"Xiuchuan He, Jingying Xie, Xiaoyang Yang, YingQun Zhou","doi":"10.1177/20552076261487229","DOIUrl":"10.1177/20552076261487229","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to systematically evaluate, through a meta-analysis, the effects of Virtual Reality (VR) interventions on depression and anxiety levels among older adults during and after the COVID-19 pandemic, and to explore variations in the effectiveness of VR interventions under different conditions.</p><p><strong>Method: </strong>Databases including PubMed, Embase, Web of Science, the Cochrane Library, CINAHL, PsycINFO, CNKI, and Wanfang Data were searched. Studies were screened according to predefined inclusion criteria, and data on study characteristics and intervention outcomes were extracted. The quality of the included studies was assessed using the appropriate risk of bias tools, and meta-analysis was performed using Review Manager (RevMan) version 5.4.1 and Comprehensive Meta-Analysis (CMA) software.</p><p><strong>Results: </strong>A total of 12 studies involving 724 older adults aged 60 years and above were included. Compared with control conditions, VR interventions significantly reduced symptoms of depression and anxiety among older adults. Exploratory subgroup analyses indicated that fully immersive VR was associated with a more pronounced pooled effect than semi- and low-immersive VR. Both shorter (<6 weeks) and longer (≥6 weeks) VR interventions were associated with significant improvements in anxiety and depressive symptoms, with no statistically significant difference between the duration subgroups.</p><p><strong>Conclusion: </strong>During and after the COVID-19 pandemic, VR interventions may represent a promising complementary non-pharmacological approach for improving depressive and anxiety symptoms among older adults. Exploratory findings suggest that immersion level may contribute to variability in intervention effects; however, these findings should be interpreted cautiously given the limited available evidence. Further high-quality, large-sample randomized controlled trials (RCTs) are needed to clarify the potential influence of intervention characteristics on VR efficacy.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261487229"},"PeriodicalIF":3.9,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148901545","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":"Global research landscape, hotspots, and emerging trends of digital biomarkers: A bibliometric analysis.","authors":"Yufei Tian, Xin Tian, Zheng Li, Zhongkai Wang, Haoxin Guo, Fanyu Meng, Zhongqing Wang","doi":"10.1177/20552076261487029","DOIUrl":"10.1177/20552076261487029","url":null,"abstract":"<p><strong>Objectives: </strong>In recent years, research interest in digital biomarkers has grown rapidly, driven by their capacity to enable continuous, objective, and personalized health monitoring. However, comprehensive bibliometric analyses of global research output in this field remain limited. This study aims to systematically evaluate the current status, hotspots, and emerging trends of global digital biomarker research using bibliometric analysis.</p><p><strong>Methods: </strong>On August 11, 2026, we retrieved publications related to digital biomarkers from the Web of Science Core Collection (WoSCC). This study encompassed articles and reviews published from January 1, 2014 to August 11, 2026. Publication years, journals, authors, institutions, countries/regions, cited references, and keywords were systematically analyzed. VOSviewer was employed to conduct co-authorship, co-occurrence, and co-citation analyses and to construct network visualization maps.</p><p><strong>Results: </strong>We evaluated a total of 1056 publications from 96 countries/regions, of which the United States was the main contributor. Harvard University, King's College London and the Massachusetts General Hospital were the primary research institutions. Among the 7,188 contributing authors, Najafi, Bijan was the most prolific, while Horak, Fay B. was the most frequently cited. Keyword cluster analysis reveals four main research topics: (1) Parkinson's disease and mobility monitoring in older adults, (2) AI-assisted diagnosis, classification, and prediction, (3) remote mental health assessment and passive physiological monitoring, and (4) cognitive dysfunction and neurodegenerative disease assessment.</p><p><strong>Conclusion: </strong>Digital biomarker research shows the characteristics of continuous expansion and increasingly diversified themes. This bibliometric analysis clarifies major research directions and knowledge structures, supporting digital biomarker development and clinical translation.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261487029"},"PeriodicalIF":3.9,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921671","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":"Experiences of using the DiabeText mobile text messaging intervention to support diabetes self-management for people living with type 2 diabetes: Post-hoc process evaluation.","authors":"Rocío Zamanillo-Campos, Xènia Chela-Álvarez, Esperança Maria Mateu-Estrany, Miquel Colom-Rosselló, Joana Maria Taltavull-Aparicio, Joana Ripoll, Maria Jesús Serrano-Ripoll, Maria Antonia Fiol-deRoque, Ignacio Ricci-Cabello","doi":"10.1177/20552076261484739","DOIUrl":"10.1177/20552076261484739","url":null,"abstract":"<p><strong>Objective: </strong>Poor adherence to prescribed treatment for people with type 2 diabetes increases the risk of complications, mortality, and associated healthcare costs. Mobile phone text-messaging interventions such as DiabeText can support diabetes self-management and improve health outcomes. This study aimed to explore patients' experiences with the DiabeText intervention and the contexts and mechanisms through which behavioral change might occur.</p><p><strong>Methods: </strong>A post-hoc process evaluation using a mixed-methods design was conducted within primary care settings in Spain. The analysis combined a questionnaire-based study with 371 participants, aimed at rating the behavior change techniques embedded in the text messages, and a qualitative interview study with 19 participants. Quantitative data were analyzed descriptively, while qualitative data underwent thematic analysis.</p><p><strong>Results: </strong>Participants reported predominantly positive experiences with the intervention, highlighting increased support, motivation, and self-care capacity. They valued the reliable and practical content as well as the user-friendly format, which were perceived as facilitating healthier lifestyle changes and supporting medication adherence. However, participants also suggested incorporating greater personalization to enhance acceptability. The mechanisms through which the intervention may foster behavioral change likely relate to the inclusion of behavior change techniques (BCTs) previously associated with positive outcomes-and which participants rated as highly helpful in supporting change. Nevertheless, further research is needed to determine the specific active ingredients that would maximize DiabeText's overall impact.</p><p><strong>Conclusion: </strong>DiabeText was well received by individuals with T2DM to support self-care. Introducing personalized features and messages that utilize BCTs previously associated with better outcomes could be a good starting point for better outcomes.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261484739"},"PeriodicalIF":3.9,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554662/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921746","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}