JMIR nursing最新文献

{"title":"Interpretation of Health-Smart Home Data and Implications for Clinical Decision-Making: Inductive Content Analysis.","authors":"Gordana Dermody, Diane J Cook, Roschelle L Fritz","doi":"10.2196/75234","DOIUrl":"10.2196/75234","url":null,"abstract":"<p><strong>Background: </strong>Health-smart home technologies offer real-time sensor-based monitoring of older adult activities of daily living, allowing for early detection of changes in health. The way clinicians interpret and use this data, particularly in visualized formats, such as bar, line, and pie graphs, remains underexplored.</p><p><strong>Objective: </strong>A qualitative descriptive study design with a quantitative component was used to explore how nurses interpret sensor-derived health data from health-smart homes in 3 cases.</p><p><strong>Methods: </strong>Using an inductive content analysis approach, we analyzed nurses' qualitative interpretations of existing sensor-derived health data from health-smart homes from 3 older adults living with ambient whole-home sensing. Nurses provided structured written feedback on visualized trends in sensor-derived health data, including activity, sleep, and mobility patterns.</p><p><strong>Results: </strong>The findings highlight both opportunities and challenges of using sensor-derived health data in older adults' care. Nurses identified key patterns in sleep, mobility, and home engagement, but interpretation difficulties, such as unclear sleep metrics and lack of clinical context, hindered decision-making. Nurses preferred bar and line graphs over pie charts for interpreting these data. Survey results show a statistically significant difference in how nurses rated different graph types (χ²2=17.1, P<.001), with pie charts rated significantly lower than both bar and line graphs (P<.001 and P=.008, respectively). These findings underscore the need for improved data visualization and integration to enhance the clinical utility of sensor-derived health data from health-smart homes.</p><p><strong>Conclusions: </strong>Findings indicate that nurses were able to provide accurate interpretations of the sensor-derived health data from health-smart homes. However, there is a need for improved visualization techniques and clinician training to optimize health-smart home data for early intervention. Standardized approaches to data representation could enhance nurses' ability to detect and act on subtle yet important information about older adults' health changes occurring in home settings.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e75234"},"PeriodicalIF":5.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12638034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575027","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":"The Role of an Intraorganizational Digital Community in Shaping Nurses' Professional Identities and Practice: Qualitative Interview Study.","authors":"Etti Rosenberg, Stefan Cojocaru","doi":"10.2196/81765","DOIUrl":"10.2196/81765","url":null,"abstract":"<p><strong>Background: </strong>In 2017, Israel's health organizations established intraorganizational social media communities, believing that they would serve as a tool that would enable people to share experiences across regional boundaries. However, conducting preliminary studies and analyzing the findings to determine how they affected employees' experience was never part of this effort.</p><p><strong>Objective: </strong>This study examined the impact of an intraorganizational digital community on nurses' professional identities and practices within a large health care organization.</p><p><strong>Methods: </strong>Using a qualitative descriptive approach, semistructured interviews were conducted with 20 nurses from various specialties and regions participating in an intraorganizational nurses' community on Facebook.</p><p><strong>Results: </strong>The findings showed that the intraorganizational community fostered a strong sense of belonging, emotional support, and professional development among its members. Participants talked about having a sense of community, much like being a member of a family, where they could confide in one another, ask for help and advice, and receive support. Enriched professional knowledge, self-efficacy, and pride in the nursing profession were all associated with active involvement in the community. The complex interactions of social media use in a hierarchical health care system were emphatically acknowledged by addressing challenges, including information overflow and concerns about sustaining a professional persona in a public digital domain.</p><p><strong>Conclusions: </strong>Overall, the study illustrated how crucial it is for health care organizations to actively manage potential negative consequences while using the benefits of intraorganizational digital networks, such as improving supportive relationships and ongoing shared learning. This study contributes to the growing body of knowledge regarding the crossroad between social media and health care, offering insights into developing strategies to promote a supportive and connected nursing workforce. The implications are particularly relevant for organizations seeking to strengthen nurse well-being and professional development through innovative digital tools. Future research should include quantitative studies to assess an intraorganizational platform's influence on outcomes such as nurses' sense of community, professional identity, self-efficacy retention, and job satisfaction.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e81765"},"PeriodicalIF":5.0,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12634009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145566492","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":"Applications of Artificial Intelligence in the Control of Infectious Diseases in the Post-COVID Era: Scoping Review.","authors":"Chanhee Kim, Robin Austin, Rebecca Wurtz, Connie White Delaney, Sripriya Rajamani","doi":"10.2196/84242","DOIUrl":"10.2196/84242","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic exposed systemic vulnerabilities in public health infrastructure, underscoring the urgency for innovation in disease surveillance and emergency response. Artificial intelligence (AI) has emerged as a promising tool to enhance the accuracy, efficiency, and scalability of public health interventions. Yet, there remains a limited understanding of how AI has been applied in real-world infectious disease control and who is contributing to its development and implementation.</p><p><strong>Objective: </strong>This scoping review aimed to map current applications of AI in public health practice for infectious disease control since 2020. Specifically, it examined (1) the types of AI tools in use, (2) their purposes and implementation contexts, and (3) the professional and institutional actors leading these efforts, including the role of nurses.</p><p><strong>Methods: </strong>Using the Joanna Briggs Institute's population, concept, and context framework, a structured search in Ovid MEDLINE was conducted, which was guided by the \"5Cs\" framework for health emergency preparedness from the World Health Organization (WHO). The search focused on English-language, peer-reviewed studies from 2020 that used AI tools for infectious disease control within real-world public health practice. Nonoriginal articles, simulation-only studies, and studies that lacked real-world implementation were excluded.</p><p><strong>Results: </strong>Out of 600 screened studies in Ovid MEDLINE, 10 met the inclusion criteria. Two major AI types were identified: machine learning (ML) algorithms and language-based tools such as chatbots and large language models. ML tools supported outbreak detection, risk stratification, and resource allocation, while language-based tools promoted health communication, particularly around immunization and HIV prevention. Studies were conducted in a diverse range of countries, including several low- and middle-income countries, and used national datasets or surveillance systems. Despite nurses comprising half of the global health workforce, no nursing-affiliated authors were found among first or corresponding authors, and no nurses were represented in the broader authorship of the included studies.</p><p><strong>Conclusions: </strong>AI technologies are being increasingly applied to support public health responses to infectious diseases, with applications ranging from predictive analytics to real-time public engagement. However, adoption remains limited in scale, scope, and professional diversity. The near-total absence of nursing participation in AI-related public health research is particularly striking and represents a missed opportunity for inclusive innovation. Strengthening implementation research and advancing informatics education among nursing professionals are critical next steps to ensure that AI tools reflect the realities of public health practice and promote equitable outcomes.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e84242"},"PeriodicalIF":5.0,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12622858/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145543954","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 the Intersection of Nursing Leadership and Artificial Intelligence: Scoping Review.","authors":"Jessica S Burford, Richard G Booth, Amanda McIntyre","doi":"10.2196/80085","DOIUrl":"10.2196/80085","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;As artificial intelligence (AI) technology permeates health care settings, nurse leaders must position themselves to shape its development, implementation, and impact, guiding meaningful change that benefits nurses and care delivery. Nurse leaders possess the capacity to influence decisions, shape practice, and ensure the delivery of ethical, safe, and high-quality care. While AI technology is reshaping many aspects of health care delivery, there is limited knowledge on how nurse leaders perceive and experience this shift.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This scoping review aimed to explore the intersection of nursing leadership and AI technology in health care by mapping current evidence, identifying key concepts, and highlighting knowledge gaps within the literature.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This scoping review was guided by the Joanna Briggs Institute methodology and reported on using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. A systematic search of 4 electronic databases (CINAHL [EBSCO Information Services], Ovid MEDLINE [Wolters Kluwer], PsycINFO [American Psychological Association], and Scopus [Elsevier]) was conducted for English-language, peer-reviewed literature published between 2014 and 2025. Gray literature was also reviewed. Articles were included if they met the inclusion criteria by exploring the population of nurse leaders and the concept of AI technology within the context of health care settings and were published in English from May 2014 forward. A total of 26 articles were included in the analysis. Qualitative content analysis and numerical summary supported the inductive identification and synthesis of data categories.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 26 articles included, 8 were empirical (qualitative, quantitative, or mixed methods), and 18 were conceptual or theoretical articles. Although 1 article was Canadian, there were no empirical studies conducted by Canadian researchers. The qualitative content analysis of the primary search findings revealed 6 overarching data categories: (1) leading digital transformation and technology integration, (2) AI technology and the nursing role: reshaping practice, (3) ethical considerations of AI technology for nurse leaders, (4) AI technology as a facilitator of innovative leadership, (5) education and training on AI technology in nursing practice, and (6) influence of AI technology on the work environment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This review confirms that nurse leaders play an essential role in shaping the future of health care in the context of AI technology. Although this review highlights a growing recognition of nursing leadership as a crucial driver of AI technology integration in health care, there is a lack of research to guide practice, policy, and leadership development through education, despite emerging interest and a recent increase i","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e80085"},"PeriodicalIF":5.0,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12617831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145524800","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":"An Internet Hospital Plus Home Nursing Model for Chronic Disease Patients: Mixed-Methods Study in Tianjin, China.","authors":"Yuzhe Wang, Yingchun Liu, Ziqi Jin, Chunjie Jin, Fang Hu, Tianzhi Yu","doi":"10.2196/76761","DOIUrl":"10.2196/76761","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Internet hospitals and \"Internet + nursing services\" represent emerging medical and nursing models in China. These platforms integrate online systems with offline care to extend services beyond traditional hospital settings. With the rapid expansion of internet hospitals, a new model-internet hospital plus home nursing-has developed. However, research on its implementation and effectiveness remains limited.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study evaluates the implementation of the internet hospital plus home nursing model by analyzing workload, patient satisfaction, and nurses' perceptions, aiming to provide a strategic reference for its further development.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Data from 2459 patients who used internet hospital plus home nursing services were collected from a hospital database. The frequency of applications and service timeliness were analyzed using χ2 tests. Patient diagnoses, service types, and geographic distribution were summarized using frequency tables and visualization techniques. A simulation approach, along with the Mann-Whitney U test, was used to compare the costs of transferring patients to hospitals versus providing home nursing. Multiple linear regression identified factors associated with cost differences. Patient satisfaction across different stages was compared using a U test, and nurses' attitudes were assessed via a questionnaire.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The majority of patients were aged 60 years and older (2120/2459, 86.2%). A significant difference in application frequency was observed across age groups (χ²4=29.86; P&lt;.001). Oncology patients were the most common users (1468/7415, 19.8%) and intravenous blood collection was the most frequent service (4899/7415, 66.1%). Most patients resided within 6 regions near the physical hospital (2119/2459, 86.2%). All patients received services within 2 days of appointment, with waiting times significantly influenced by appointment timing (χ²1 = 290.88; P&lt;.001). Cost distributions varied significantly by gender, age, service frequency, distance, and service type (all P&lt;.001), with service type and distance identified as key cost determinants (P&lt;.001). Patient satisfaction was consistently high across periods, with no significant difference (Mann-Whitney U=5,090,149; P=.38). Nurses expressed positive perceptions of the model.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The internet hospital plus home nursing model effectively combines online diagnosis with in-home care, creating a closed-loop service that improves accessibility-particularly for older adults and mobility-impaired patients. Pilot implementations in Tianjin demonstrated benefits in convenience, accessibility, and cost-effectiveness, alongside high patient satisfaction and nursing staff approval. Given the aging population, this model holds significant potential for broader adoption. To ensure sustainable development, enhanced safety mechanisms and policy sup","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e76761"},"PeriodicalIF":5.0,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12588391/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145453846","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":"Validation of the Perception of eHealth Technology Scale in Chinese Brief (PETS-C Brief) in Nurses: Survey Study.","authors":"Ayisha Jilili, Xue Weng, Palida Maimaiti, Liwen Liao, Sheng Zhi Zhao, Lin Wang, Ningyuan Guo","doi":"10.2196/79594","DOIUrl":"10.2196/79594","url":null,"abstract":"<p><strong>Background: </strong>eHealth technologies have shown promise in improving the accessibility and quality of nursing research and practice. Little is known about nurses' perception of eHealth technology that are prerequisites for the implementation of eHealth-based nursing care.</p><p><strong>Objective: </strong>We aimed to confirm the factor structure and examine the validity and reliability of the novel 19-item Perception of eHealth Technology Scale in Chinese Brief (PETS-C Brief) in Chinese nurses. The associations of sociodemographic and working-related characteristics with PETS-C Brief scores were investigated.</p><p><strong>Methods: </strong>Participants were 1409 nurses (96.8% female; mean age 34.6, SD 8.6 y) working in hospital or community settings in Shanghai, China. Confirmatory factor analysis was conducted to verify the previously reported four-factor structure of PETS-C Brief. Cronbach α was calculated for internal consistency reliability. One-month test-retest reliability was assessed in 123 participants completing the one-month follow-up survey. Associations of sociodemographic and working-related characteristics (ie, years of employment, professional title, and setting) with PETS-C Brief scores were analyzed using multivariable linear regression. Known-group validity was assessed by examining the associations of age and educational attainment with PETS-C Brief scores.</p><p><strong>Results: </strong>The goodness-of-fit of the four-factor PETS-C Brief was shown to be acceptable (comparative fit index [CFI]=0.95, standardized root mean squared residual [SRMR]=0.065, root mean square error of approximation [RMSEA]=0.074). The scale showed a good internal consistency reliability (Cronbach α=0.91) and one-month test-retest reliability (intraclass correlation coefficient=0.68, 95% CI: 0.55, 0.78). Known-group validity was supported by the inverse association of age with PETS-C Brief scores (P=.002) and positive association of educational attainment with PETS-C Brief scores (P for trend=.043). No significant associations were observed between working-related characteristics and PETS-C Brief scores.</p><p><strong>Conclusions: </strong>Our validation study supported the four-factor structure of PETS-C Brief with satisfactory validity and reliability in Chinese nurses, suggesting the scale could be deployed for assessing perception of eHealth technology. Future studies with larger sample, random sampling, and in other cultural settings are warranted to increase the generalizability.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e79594"},"PeriodicalIF":5.0,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12588618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145453865","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":"Determinants of Nurses' Continuance Intention to Use Mobile Health Apps in Clinical Nursing Practice: Structural Equation Modeling to Extend the Expectation-Confirmation Model.","authors":"Narjes Mirabootalebi, Felix Holl, Zahra Meidani, Fatemeh Rangraz Jeddi, Zahra Tagharrobi, Hossein Akbari, Walter Swoboda","doi":"10.2196/68048","DOIUrl":"10.2196/68048","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mobile health (mHealth) apps enhance clinical nursing by improving access to resources and patient care. Further benefits include reduced errors, time savings, better communication, cost reduction, and training. Understanding factors driving nurses' continued mHealth adoption is key to its sustained success.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study extends the expectation-confirmation model (ECM) to explore the determinants of Iranian nurses' continuance intention to use mHealth apps in their daily clinical routines.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A cross-sectional, descriptive-analytical study was conducted among 315 nurses from hospitals affiliated with Kashan University of Medical Sciences. The Nurses' Mobile Health Device Acceptance Scale (NMHDA-Scale) was developed by the authors in 2022. The Intention to Continue Using Mobile Health Applications for Nurses questionnaire assesses nurses' future willingness to use mHealth apps in their practice. This questionnaire was designed based on the ECM and the approach by Waltz et al. Its primary aim is to identify the factors that influence mHealth device acceptance, specifically among clinical nurses, as previous studies have not focused on this group and have shown inconsistent relationships between various factors. Participants completed structured questionnaires measuring perceived usefulness, perceived ease of use, social influence, habits, and technology anxiety. Data were analyzed using structural equation modeling in AMOS (version 26). The model tested relationships among confirmation, perceived usefulness, social influence, technology anxiety, and mHealth continuance behavior.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The analyzed sample (n=315) consisted primarily of female (252/315, 80%) and married (243/315, 77.1%) nurses, with a mean age of 35.67 (SD 1.24) years. The analysis revealed that perceived usefulness was significantly influenced by both confirmation (P&lt;.001) and social influence (P&lt;.001). Perceived ease of use was negatively impacted by new technology anxiety (P&lt;.001), indicating that higher anxiety levels reduced perceived ease of use. Additionally, mHealth continuance behavior was positively associated with habits (P=.002), social influence (P&lt;.001), and perceived security risks (P=.008). Contrary to expectations, perceived usefulness did not directly influence mHealth continuance (P=.15), suggesting that other factors, such as habits and social influence, play a more significant role in long-term use.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Sustained mHealth app use by nurses hinges more on social influence and confirmed expectations than perceived usefulness. Although new technology anxiety remains a barrier, habits and social influence are key to long-term adoption. Hospital leaders should prioritize strategies that foster positive social reinforcement, minimize security concerns, and reduce anxiety through training and support when integrating mHeal","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e68048"},"PeriodicalIF":4.0,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12578357/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145423767","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":"Self-Health Monitoring by Smart Devices and Ontology Technology for Older Adults With Uncontrolled Hypertension: Quasi-Experimental Study.","authors":"Sutteeporn Moolsart, Khajitpan M Kritpolviman","doi":"10.2196/73386","DOIUrl":"10.2196/73386","url":null,"abstract":"<p><strong>Background: </strong>Hypertension is a prevalent concern among older adults, often leading to complex cardiovascular complications when uncontrolled. Telenursing technology facilitates self-management, and the integration of domain-specific ontology allows real-time interpretation of behavioral and biometric data to provide personalized recommendations, enhancing patient engagement and self-care.</p><p><strong>Objective: </strong>This study aimed to examine the within-group and between-group effects of self-health monitoring using smart devices combined with ontology technology on hypertension-controlling behavior and mean arterial pressure among older adults with uncontrolled hypertension.</p><p><strong>Methods: </strong>The quasi-experimental design was conducted with 91 older adults in Bangkok, Thailand (46 experimental and 45 comparison participants). Participants in the experimental group used the \"HT GeriCare@STOU\" app on smartphones, linked to smartwatches for blood pressure monitoring, step count, and sleep pattern, with telenursing support via video calls. Data on hypertension-controlling behavior were collected using a validated questionnaire (Cronbach α=0.83; content validity index=0.98). Descriptive statistics and t tests were used to analyze within-group and between-group differences.</p><p><strong>Results: </strong>Within-group analysis revealed that experimental participants showed improved hypertension-controlling behavior and reduced mean arterial pressure after the program. Between-group comparisons indicated that mean arterial pressure in the experimental group was significantly lower than in the comparison group (P<.05), although hypertension-controlling behavior did not differ significantly between groups. Older adult participants and nurses reported high satisfaction, noting that real-time feedback increased awareness of blood pressure and motivated independent health behavior adjustments.</p><p><strong>Conclusions: </strong>Self-health monitoring using smart devices integrated with ontology technology effectively improved physiological outcomes and supported self-management in older adults with uncontrolled hypertension. The ontology framework enabled personalized, real-time decision support, highlighting its novelty, and potential to enhance nursing practice. Future studies with larger samples and longer follow-up are recommended to further evaluate the intervention's effectiveness and scalability.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e73386"},"PeriodicalIF":4.0,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12520622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294614","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":"First Contact With Care Through Clinical Simulation in Nursing Students: Qualitative Study.","authors":"Eva García Carpintero-Blas, Pablo Del Pozo-Herce, Maria Del Carmen Hernández-Cediel, Marta Rodríguez-García, Noelia Navas-Echazarreta, Elena Chover-Sierra, Antonio Martínez-Sabater, Raúl Juárez-Vela, Alberto Tovar-Reinoso","doi":"10.2196/81617","DOIUrl":"10.2196/81617","url":null,"abstract":"<p><strong>Background: </strong>Clinical simulation with standardized patients provides nursing students with their first approach to care in a safe and realistic environment. This type of experience arouses intense emotions and supports the development of key competencies.</p><p><strong>Objective: </strong>This study aimed to explore the perceptions of nursing students during their first contact with care through simulation experiences with standardized patients.</p><p><strong>Methods: </strong>We conducted a qualitative descriptive phenomenological study using focus groups and reflective narratives with a sample of 59 students. A thematic analysis was performed using ATLAS.ti (version 24; Scientific Software Development GmbH).</p><p><strong>Results: </strong>Three thematic blocks, along with their categories and subcategories, were identified: (T1) first contact with simulation, (T2) learning and competencies, and (T3) preparation for clinical practice.</p><p><strong>Conclusions: </strong>Clinical simulation has a profound impact on nursing education by offering a safe and realistic environment in which to learn from experience. Emotions, teamwork, and human interaction enrich learning and strengthen professional identity. This approach supports the integration of technical knowledge and relational skills. The results support the inclusion of active and humanized methodologies in training plans.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e81617"},"PeriodicalIF":5.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12531447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145310151","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":"Socio-technical Needs of Registered Nurses in the Heart Failure Hospitalizations of African Americans: A Cross-sectional Study.","authors":"Tremaine Brueon Williams, Milan Bimali, Maryam Y Garza, Pearman Parker, Chase Paladino-Vaden, Emel Seker, Alisha Crump, Randy Rice, Latrina Prince, Taren Massey-Swindle, Kevin Wayne Sexton","doi":"10.2196/75080","DOIUrl":"10.2196/75080","url":null,"abstract":"<p><strong>Background: </strong>African Americans are disproportionately impacted by congestive heart failure (CHF). The impact includes a two and a half times greater hospitalization rate and a fourth of a day longer length of hospitalization than Caucasians, of which nursing care has been associated with nearly a 30% decrease in hospitalizations and readmissions. Prior studies have demonstrated that registered nurses (RNs), working in conjunction with electronic health record systems (EHRs) to conduct care tasks, may optimize length of stay in African Americans with CHF.</p><p><strong>Objective: </strong>The objective of the study was to identify the needs of RNs who performed socio-technical tasks, the perceived importance of these socio-technical tasks, and the perceived performance of these tasks by RNs, in relation to the length of stay of their African American patients with CHF.</p><p><strong>Methods: </strong>The study employed an observational, cross-sectional survey design in RNs who were randomly selected from a total population of 3,498 RNs who provided care to 22,703 African Americans with CHF within 113,543 heart failure hospitalizations between January 1, 2015, and January 1, 2024. The RNs were retrospectively stratified into two groups based on EHR data: those whose African American patients had a mean length of stay of 10 days or less (Group A) and those whose mean length of stay was greater than 10 days (Group B). Descriptive statistics, Cohen's d, and a two-sided unpaired t-test were used to analyze the data.</p><p><strong>Results: </strong>The total sample of 200 RNs responded to the survey (100% survey completion rate). Group A (100 RNs) reported the least important task as drawing conclusions about how to use the EHR to care for African Americans (Mean=4.66, SD=1.82). The least important task in Group B (100 RNs) was reading published research on African Americans (mean=4.88, SD=1.70). Group A reported performing best in caring for African American patients (Mean=5.61, SD=1.44). Group B reported performing best at caring for all patients (Mean=5.86, SD=1.04). A total of seventeen significant socio-technical needs were identified among groups. Two socio-technical needs were unique to Group B: caring for patients (i.e., the full scope of social and technological processes in nursing care) (Cohen's d=0.32, 95% CI: 0.04,0.59, P=.04) and working with information related to a patient's CHF in the EHR (e.g., laboratory results, discharge summaries, or radiographic images) to care for the patient (Cohen's d=0.33, 95% CI: 0.05,0.61, P=.03).</p><p><strong>Conclusions: </strong>Lengths of patient stay may be reduced by identifying and addressing socio-technical needs through targeted training, nursing care interventions, and RN-led risk stratification guidelines for working with EHRs to reduce lengths of stay in those who are disproportionately impacted by CHF.</p><p><strong>Clinicaltrial: </strong>N/a.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145380066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}