JMIR nursing最新文献

{"title":"Nursing Process Data for Health Care Cost Prediction Using Machine Learning: Longitudinal Study.","authors":"María Consuelo Company-Sancho, Víctor M González-Chordá, María Isabel Orts-Cortés","doi":"10.2196/93638","DOIUrl":"10.2196/93638","url":null,"abstract":"<p><strong>Background: </strong>Machine learning (ML) has been demonstrated to enhance health care cost prediction by handling high-dimensional data and identifying complex patterns. However, current risk-adjustment models rarely incorporate structured nursing information derived from the nursing process. This information captures care needs and human responses to health problems.</p><p><strong>Objective: </strong>This study aimed to evaluate the impact of integrating nursing process data into ML-based predictive models of individual health care costs, including cost component analyses, compared with models based solely on sociodemographic, clinical, and morbidity-related variables.</p><p><strong>Methods: </strong>A retrospective observational study was conducted using a population-based cohort of 1,691,075 individuals aged 15 years or younger who were registered with the Canary Islands Health Service. Predictors were derived from data available up to 2017 and included sociodemographic and clinical variables, Adjusted Morbidity Groups, health care use, and structured nursing records (Functional Health Patterns [FHP], North American Nursing Diagnosis Association [NANDA], Nursing Outcomes Classification [NOC], and Nursing Interventions Classification [NIC]). Predictive models were developed using feedforward neural networks and extreme gradient boosting; predictions were combined using an ensemble approach. An autoencoder was applied as a dimensionality-reduction technique for the nursing variables. Model performance with and without nursing variables was compared on total cost and individual cost components, and the coefficient of determination (<i>R</i>²) was used on the test set.</p><p><strong>Results: </strong>Including the nursing methodology yielded small numerical increases in predictive performance. With respect to total cost, the ensemble model improved the <i>R</i>² from 0.5023 to 0.5058 when the nursing variables were added. Although directionally consistent, these gains were limited in magnitude. In the component-level analyses, performance gains were observed in hospital care (<i>R</i>²=0.2396) and pharmaceutical costs (<i>R</i>²=0.6631). Reducing 789 nursing variables to 16 latent dimensions using an autoencoder substantially simplified the feature space, with predictive performance remaining broadly comparable but without a substantial additional gain.</p><p><strong>Conclusions: </strong>Integrating structured information from the nursing process is associated with small incremental improvements in ML-based predictive models and complements commonly used sociodemographic, clinical, and morbidity variables. The systematic incorporation of nursing data into predictive tools may contribute to more accurate health care cost prediction and support more holistic, person-centered approaches.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e93638"},"PeriodicalIF":5.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13544411/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893114","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":"Train-the-Trainer Evaluation of Technology-Enhanced Learning in Preservice Midwifery Education in Nigeria: Qualitative Descriptive Study.","authors":"Isabella Garti, Nadyen Shikpup, Ophelia Tonto, Michelle Gray, Halima Musa Abdul, Awwal Mohammed Ladan","doi":"10.2196/93596","DOIUrl":"10.2196/93596","url":null,"abstract":"<p><strong>Background: </strong>Midwifery education in Nigeria is undergoing a transformation in pedagogical approach. Existing traditional teaching materials are being supplemented by digital technology-enhanced learning resources; however, educators' limited pedagogical capacity may hinder their adoption. Thus, train-the-trainer (TTT) workshops were implemented to build capacity among midwifery educators to prepare them for the implementation of a digital learning platform across 20 institutions in Nigeria.</p><p><strong>Objective: </strong>This study aimed to describe the development and qualitative evaluation of a TTT program to support the implementation of a technology-enhanced learning platform within preservice midwifery education in Nigeria. We aimed to evaluate participants' perceptions of the training and their preparedness to implement their learning.</p><p><strong>Methods: </strong>The workshop was developed using the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) instructional design model. A 3-day TTT program was delivered in 2 cohorts in July 2025 to a purposive sample of 80 institution-nominated midwifery educators and information and communication technology officers from 20 pilot institutions. A qualitative descriptive study was conducted to explore participants' perceptions of the training and their readiness to implement and support the technology-enhanced learning platform within their institutions. Data were analyzed using qualitative content analysis.</p><p><strong>Results: </strong>Pre-workshop responses demonstrated varying levels of prior exposure to and use of digital technologies, as well as differing expectations of the training. Four themes emerged from the post-workshop evaluation. Participants perceived improvements in their understanding of pedagogical and technology-enhanced learning approaches; reported greater readiness to support implementation and knowledge transfer within their institutions; highlighted infrastructure and technical support barriers to implementation; and emphasized the need for ongoing capacity building, monitoring, and support to promote long-term sustainability.</p><p><strong>Conclusions: </strong>Rather than evaluating educational outcomes, this study provides early evidence of educator readiness, implementation considerations, and sustainability requirements for technology-enhanced learning in preservice midwifery education in low-resourced settings such as Nigeria. A multifaceted approach combining capacity building, ongoing support, technical requirements, and institutional commitment to continuous improvement is needed. Future phases of this implementation research will evaluate educational, implementation, and institutional outcomes associated with the digital learning platform.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e93596"},"PeriodicalIF":5.0,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13529257/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882824","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":"AI Use, Perceptions, and Perceived Impact Among Nursing Students: Cross-Sectional Study.","authors":"Ilda Taka, Elona Hasalla, Albana Sula, Blerina Bahiti, Rajmonda Oboni, Blerina Bani","doi":"10.2196/97832","DOIUrl":"10.2196/97832","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;AI is increasingly being integrated into education and health care, offering opportunities to improve learning, understanding of clinical cases, and students' self-confidence. However, it remains necessary to assess nursing students' perceptions of AI and its impact on their academic and professional development.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of the study was to assess AI use among nursing students, their perceptions of AI, and its impact on learning, academic performance, and professional preparation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A descriptive cross-sectional study with analytical components was conducted at the Faculty of Medical Technical Sciences in Elbasan, Albania. Data were collected through a structured questionnaire administered via Google Forms, which assessed AI use and its perceived impact on learning and professional preparation. Data were analyzed using SPSS (version 23.0). Descriptive statistics, chi-square tests for associations between variables (&lt;i&gt;P&lt;/i&gt;&lt;.05), and logistic regression to estimate crude odds ratios (CORs) and adjusted odds ratios (AORs) with 95% CIs were used.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 279 nursing students participated in the study (mean age 22.4, SD 5.7 years), the majority of whom were female (273/279, 97.8%), lived in urban areas (156/279, 55.9%), and were enrolled in the bachelor's program (198/279, 71%). Overall, 83.9% (234/279) reported using AI, mainly virtual assistants such as ChatGPT or similar tools (183/234, 78.2%). The most common reason was information searching (216/234, 92.3%), followed by studying and understanding lecture content (96/234, 41%). Bivariate analysis showed no significant associations between AI use and residence or study cycle, whereas grade point average (GPA) was significantly associated with AI use. In the multivariable analysis, GPA remained the only independent predictor of AI use. Students with a GPA of 6.0 to 6.9 (AOR 5.55, 95% CI 1.53-20.14; &lt;i&gt;P&lt;/i&gt;=.009) and those with a GPA of 8.0 to 8.9 (AOR 5.73, 95% CI 1.26-26.00; &lt;i&gt;P&lt;/i&gt;=.02) were significantly more likely to use AI than the reference group. Students perceived AI as having a moderate impact on learning, particularly understanding lectures (mean 2.58, SD 1.10) and exam preparation (mean 2.58, SD 1.02), whereas its impact on self-confidence (mean 1.97, SD 1.17) and empathy (mean 1.90, SD 1.12) was perceived as low. Although AI was considered useful for supporting learning (mean 2.83, SD 1.12), students expressed concerns regarding the reliability of AI-generated information (mean 3.18, SD 1.22), dependence on AI (mean 2.75, SD 1.28), and its impact on critical thinking (mean 2.80, SD 1.18).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;AI is widely used among nursing students, primarily supporting learning and academic performance. However, its impact on professional and interpersonal competencies remains limited. These findings suggest the need for integrating AI i","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e97832"},"PeriodicalIF":5.0,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13529259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882805","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":"A One-Year Study Using Digital Biomarkers From Sensing Technologies to Assess Changes in Physical Activity Levels and Sleep Quality in Nursing Home Residents With Dementia: Observational Study.","authors":"Lydia D Boyle, Monica Patrascu, Bettina S Husebo, Kristoffer Haugarvoll, Ole Martin Steihaug, Brice Marty","doi":"10.2196/95194","DOIUrl":"10.2196/95194","url":null,"abstract":"<p><strong>Background: </strong>Proxy-rated questionnaires remain the standard for the assessment of activity and sleep for people with dementia living in nursing homes. Sensing technologies, such as wearables, can generate continuous data that provide quantitative insights into daily activities and behavioral and psychological symptoms, such as sleep disturbance. This study explores the use of sensing technologies in the detection of changes in physical activity levels and sleep behaviors over time.</p><p><strong>Objective: </strong>This study aims to explore the long-term capabilities of multimodal sensing technologies for assessing physical activity levels and sleep quality using selected digital biomarkers for nursing home residents with dementia. Objectives were for observation to be aligned with real-world conditions in which such sensing technologies would be applied within a nursing home environment and to assess whether distinct differences in selected digital biomarkers can be observed accurately and reliably longitudinally.</p><p><strong>Methods: </strong>This study included 11 participants (79-93 y) recruited from 2 dementia care units in Norway. A smartwatch (Garmin Vivoactive 5 or Garmin Venu 3) and a radar-based system (Vital Things, Somnofy) were used to collect 7 days and 6 nights of data on physical activity levels and sleep quality at baseline, 6 months, and 1 year. The Personal Self-Maintenance Score and Neuropsychiatric Inventory-Nursing Home Version (nighttime behaviors section K) were also administered. Digital biomarkers included Euclidean norm minus one (ENMO), sleep efficiency (SE), wake after sleep onset (WASO), sleep regulatory index (SRI), sleep fragmentation index (SFI), total sleep time (TST), and time out of bed (no presence).</p><p><strong>Results: </strong>A total of 9 participants were included in the final analysis. Differences were found in nighttime ENMO (<i>P</i>=.01) and in 4 of the sleep biomarkers: TST (<i>P</i>=.02), SE (<i>P</i>=.02), WASO (<i>P</i>=.01), and SRI (<i>P</i>=.01). The long-term reliability of the group ENMO was poor (intraclass correlation coefficient 0.00-0.02); however, it was moderate to strong (0.58-0.79) between individuals at each time point. The adherence and acceptability of the technologies was high (88%-96%), and the application of the devices was well tolerated by the participants, with no adverse events.</p><p><strong>Conclusions: </strong>The use of sensing technologies could enable more objective, data-driven future care models for people with dementia residing in nursing homes; however, the results emphasized in this study require the recommendation for cautious, well-designed use of digital biomarkers for clinical decision-making.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e95194"},"PeriodicalIF":5.0,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13524361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882859","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":"Radar-Based Contactless Monitoring in Different Inpatient Nursing Care Settings: Mixed Methods Feasibility Study.","authors":"Madeleine Ritter-Herschbach, Sebastian Hofstetter, Dominik Behr, Melina Hager, Burkhard Mueller, Patrick Jahn","doi":"10.2196/93624","DOIUrl":"10.2196/93624","url":null,"abstract":"<p><strong>Background: </strong>Monitoring of vital signs is a critical component of nursing care, facilitating the early detection of complications, enabling the observation of the progression of health status, and providing a foundation for diagnosis and therapeutic decision-making. It is instrumental in ensuring quality of care. Contactless measurement methods, such as radar-based sensors, have the potential to enable continuous monitoring of respiration, heart rate, and bed activity without disturbing or stressing vulnerable patients.</p><p><strong>Objective: </strong>The study investigates the feasibility, usability, acceptance, and implementation of a contactless digital vital-signs monitoring system. The objective is to ascertain the suitability of the device for incorporation into the workflow of nursing personnel and to assess implementation factors in an inpatient setting. Additionally, the study examines user acceptance and satisfaction among nursing staff.</p><p><strong>Methods: </strong>A total of 13 nursing staff members from geriatric and dialysis departments participated in this mixed methods pilot study. Following the installation of the technology, the participants underwent a 30-minute training period to become acquainted with its operation. Thereafter, the system was subjected to a 4-week trial period. Quantitative data were collected at 3 points in time using the Technology Usage Inventory and the System Usability Scale. Furthermore, measurement protocols were evaluated to compare the time required for the standard and contactless measurement methods. Qualitative data were collected subsequent to the conclusion of the trial through the implementation of focus group interviews. Ethical approval and informed consent were obtained.</p><p><strong>Results: </strong>The study results suggest that the average intention to use among the study participants (n=13) reached a relatively high level after 4 weeks (mean 85.5, SD 107.7). However, variation was observed when the 2 nursing wards were considered separately (geriatrics: 28.4 vs dialysis 199.8). The monitoring system was evaluated based on the System Usability Scale, which yielded a mean score of 74.5 (SD 10.5), indicating good usability. The nurses reported a perceived enhancement in their sense of security and monitoring oversight during routine care. Additionally, potential time savings were observed due to a reduction in disruptions to established processes. However, compared with conventional real-time monitoring, disadvantages were identified and technical requirements were defined.</p><p><strong>Conclusions: </strong>The use of contactless measurement in the assessment of vital signs holds considerable promise for enhancing the efficacy of nursing care, particularly in contexts where manual measurements have been the prevailing standard. In order to further explore technical capabilities in a nursing context, additional research needs to be conducted.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e93624"},"PeriodicalIF":5.0,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13524219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882792","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":"Manifestations and Potential Consequences of Information Distortion in Electronic Nursing Records: Qualitative Study.","authors":"Jianan Wang, Yihong Xu, Wen Li, Zhichao Yang, Yushu Sun, Xiaoxiao Zhang, Hongying Pan","doi":"10.2196/96679","DOIUrl":"10.2196/96679","url":null,"abstract":"<p><strong>Background: </strong>Nursing records are a critical reflection of nursing practices and play a key role in ensuring the quality of patient care. However, these records sometimes fail to accurately represent the real-world health truth, resulting in information distortion. The manifestations and potential consequences of such distortion remain understudied, which may hinder efforts to improve nursing practices.</p><p><strong>Objective: </strong>This study aims to explore the primary manifestations and potential consequences of information distortion in electronic nursing records (ENRs).</p><p><strong>Methods: </strong>We conducted semistructured interviews at a Class-A tertiary hospital in China. A total of 12 nurses from various care units were recruited through purposive and snowball sampling. Inductive content analysis was used to analyze the qualitative data.</p><p><strong>Results: </strong>Qualitative analysis revealed that manifestations of information distortion in ENRs fall into 4 main categories: untimeliness, inaccuracy, incompleteness, and inconsistency. These categories are further subdivided into 7 subcategories: premature recording, delayed recording, incorrect data entry, fabrication, account misuse, missing data, and inconsistent data entry. The potential consequences of information distortion encompass 3 main categories: patient-related, nurse-related, and data-related. These categories are further subdivided into 5 subcategories: threats to patient safety, increases in negative emotions, damage to professional image, erosion of nursing professionalism, and impairment of secondary data use.</p><p><strong>Conclusions: </strong>The identified manifestations encompass a wide range of nursing activities, with potential consequences affecting patients, research, and caregivers. Strengthening ethics education, implementing monitoring mechanisms, establishing standardized guidelines, and leveraging technological solutions are recommended to reduce information distortion in ENRs. Further research on contributing factors and effective countermeasures is needed to mitigate information distortion in ENRs.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e96679"},"PeriodicalIF":5.0,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13509441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148835505","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":"Multicomponent Supportive-Educative mHealth Intervention for Self-Care Among Individuals With Osteoporosis in Thailand: Exploratory Pretest-Posttest Pilot Study.","authors":"Natthida Pangutha, Sirinapha Jittimanee, Porramat Saksaen","doi":"10.2196/101025","DOIUrl":"https://doi.org/10.2196/101025","url":null,"abstract":"<p><strong>Background: </strong>Poor self-care impairs osteoporosis management. Disease-related knowledge and family support have influenced self-care; however, the combined association of these factors with self-care remains insufficiently understood.</p><p><strong>Objective: </strong>This study aimed to compare self-care among individuals with osteoporosis who received a multicomponent supportive-educative mobile health intervention with that of individuals who received usual care.</p><p><strong>Methods: </strong>This exploratory pilot study using a pretest-posttest design with a nonequivalent control group included 46 individuals aged 50 years and older who had been diagnosed with osteoporosis and were receiving care at the orthopedic outpatient department of a university hospital in Bangkok. Participants were matched by sex, age, and back pain scores and then sequentially allocated to an intervention group (n=23, 50%) and a comparison group (n=23, 50%). The intervention group received a 4-week multicomponent supportive-educative intervention based on Orem's self-care theory, delivered partly through a closed Facebook group. The intervention included educational guidance, video-based teaching, psychological support, family involvement, and remote nurse interaction. The comparison group received usual care. Self-care was measured using the Self-Care of Osteoporosis Scale. Chi-square tests, Fisher exact tests, independent-sample 2-tailed <i>t</i> tests, analysis of covariance, and multivariate analysis of covariance were used to examine differences between groups.</p><p><strong>Results: </strong>After controlling for baseline self-care scores, participants in the intervention group had significantly higher postintervention self-care scores than those in the comparison group (<i>F</i>_1,43=459.29; <i>P</i><.001). Significant group differences were also observed across all 3 domains of self-care after controlling for baseline scores. Compared with the control group, the intervention group demonstrated significantly higher postintervention scores in diet and physical activity (<i>F</i>_1,41=245.04; <i>P</i><.001), safety performance (<i>F</i>_1,41=171.20; <i>P</i><.001), and medication adherence (<i>F</i>_1,41=144.54; <i>P</i><.001).</p><p><strong>Conclusions: </strong>The multicomponent supportive-educative intervention delivered partly through Facebook was associated with improvements in self-care among individuals with osteoporosis. Future rigorous trials are needed to confirm its efficacy.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e101025"},"PeriodicalIF":5.0,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13492480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802721","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 and Feasibility of a Consumer-Grade Wearable Sleep Monitoring Device and an Exploratory Evaluation of Sleep Characteristics and Associated Factors Among Nurses: Observational Cohort Study.","authors":"Siew Hoon Lim, Fazila Aloweni, Phyllis Ong, Shin Yuh Ang","doi":"10.2196/96912","DOIUrl":"10.2196/96912","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Shift work disorder and insufficient sleep are prevalent among nurses, leading to fatigue, reduced well-being, and potential safety concerns. The increasing use of wearable sleep-tracking devices presents an opportunity to evaluate nurses' sleep quality objectively.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The primary objective was to evaluate the feasibility of wearable-based sleep monitoring and to obtain preliminary evidence of agreement with validated actigraphy among nurses. The secondary objective was to describe nurses' sleep characteristics and to examine exploratory associations between sociodemographic characteristics, shift patterns, sleep hygiene, and sleep parameters.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A 2-phase feasibility observational cohort study was conducted in a tertiary hospital in Singapore. In phase 1, 5 nurses concurrently wore a consumer-grade, wrist-worn Apple Watch Series 10 and a validated actigraph (GENEActiv; ActivInsights Ltd) for 2 weeks. Preliminary agreement between the Apple Watch and GENEActiv was examined using intraclass correlation coefficients. Feasibility was determined through wear-time compliance and data completeness. In phase 2, 50 nurses working rotating or single shifts completed demographic and work-related questionnaires and the Sleep Hygiene Index. Multiple linear regression analyses were performed to examine exploratory associations between selected covariates and sleep parameters, with adjustment for age, sex, BMI, parental status, workplace, total length of service, and sleep hygiene.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The Apple Watch showed preliminary evidence of agreement with GENEActiv for total sleep time, in-bed wake time, and sleep efficiency (intraclass correlation coefficients of 0.95, 0.72, and 0.69, respectively), with high wear compliance and minimal missing data, supporting its feasibility for sleep monitoring. Mean total sleep time was 381 (SD 55) minutes, and mean sleep efficiency was 94.77% (SD 4.11%). Shift nurses reported poorer sleep hygiene than nonshift nurses; however, shift work status was not independently associated with sleep outcomes after adjustment. Higher BMI was associated with shorter total sleep time (unstandardized coefficient &lt;i&gt;B&lt;/i&gt;=-3.76 min/kg/m²; &lt;i&gt;P&lt;/i&gt;=.01), reduced rapid eye movement sleep (unstandardized coefficient &lt;i&gt;B&lt;/i&gt;=-1.18 min; &lt;i&gt;P&lt;/i&gt;=.03), shorter core sleep (unstandardized coefficient &lt;i&gt;B&lt;/i&gt;=-2.72 min; &lt;i&gt;P&lt;/i&gt;=.02), and reduced time in bed (unstandardized coefficient &lt;i&gt;B&lt;/i&gt;=-4.05 min; &lt;i&gt;P&lt;/i&gt;&lt;.01=.008). Age was negatively associated with deep sleep duration, with older age associated with less deep sleep (unstandardized coefficient &lt;i&gt;B&lt;/i&gt;=-1.00 min/y; &lt;i&gt;P&lt;/i&gt;=.003).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Apple Watch-based monitoring was feasible and showed preliminary agreement with actigraphy. Nurses slept less than recommended, and BMI and age were associated with sleep outcomes in exploratory analyses. These fi","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e96912"},"PeriodicalIF":5.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13488918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802697","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":"Factors Influencing Nursing Internship Students' Readiness to Use AI: Cross-Sectional Study Using Neural Network Analysis.","authors":"Sameer A Alkubati, Wesam T Almagharbeh, Talal A Alqalah, Basma Salameh, Hamdan Albaqawi, Awatif M Alrasheeday, Abdulhafith Alharbi, Layla Alshammari, Bushra Alshammari, Eddieson Pasay-An, Anwar Abdulkareem, Shimmaa Mohammed Elsayed","doi":"10.2196/92533","DOIUrl":"https://doi.org/10.2196/92533","url":null,"abstract":"<p><strong>Background: </strong>Enhancing nursing students' awareness, attitudes, beliefs, and preparedness toward AI may help improve their health care knowledge and practice.</p><p><strong>Objective: </strong>This study aimed to assess nursing students' attitudes, perceptions, self-efficacy, barriers, and anxiety, which influence their readiness to adopt AI in nursing practice.</p><p><strong>Methods: </strong>This study used a cross-sectional, correlational design. Data were collected from 307 nursing internship students using an 8-part, self-administered questionnaire.</p><p><strong>Results: </strong>Increased self-efficacy with computers was correlated with decreased barriers to accessing AI technology, lower computer anxiety scale scores (<i>r</i>=-0.27, <i>P</i><.001 and <i>r</i>=-0.57, <i>P</i><.001, respectively), and higher perceptions of using AI (<i>r</i>=0.27, <i>P</i><.001). Meanwhile, nursing students' readiness to adopt AI in nursing practice was negatively associated with barriers to accessing AI technology (<i>r</i>=-0.20, <i>P</i><.001) and positively associated with attitudes toward and perceptions of using AI (<i>r</i>=0.32, <i>P</i><.001 and <i>r</i>=0.14, <i>P</i>=.01, respectively). Increased barriers to accessing AI technology were associated with negative attitudes toward AI and nursing students' perceptions of using AI (<i>r</i>=-0.34, <i>P</i><.001 and <i>r</i>=-0.39, <i>P</i><.001, respectively). A multilayer neural network model identified barriers (relative importance=0.27), attitudes (relative importance=0.16), and perceptions (relative importance=0.15) as the most significant predictors, while self-efficacy (relative importance=0.11) and anxiety (relative importance=0.07) showed smaller contributions, despite nonsignificant bivariate associations with nursing students' AI readiness. The model demonstrated strong predictive performance, achieving a low relative error of 0.62 in the training set. The stability and generalization ability of the model were supported by the training and testing set results, which yielded a training sum of squares error of 65.93 and a testing sum of squares error of 35.49, showing no signs of overfitting.</p><p><strong>Conclusions: </strong>Several contributing factors influenced nursing students' readiness to embrace AI, with barriers, attitudes, and perceptions emerging as the most consistent, whereas self-efficacy and anxiety may play indirect roles. To improve the adoption of AI among nursing students, such factors should be dealt with in such educational programs; an interrelated adoption of AI in nursing practice is expounded as a more favorable environment.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e92533"},"PeriodicalIF":5.0,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13484946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802663","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":"Web-Based Mindfulness Intervention for Reducing Perceived Stress in Health Care Professionals: Secondary Analysis of Moderators in a Stepped-Wedge Cluster Randomized Trial.","authors":"Gloria Guerrero-Pertiñez, Vera Carbonell Aranda, Jonathan Joseph Dawood-Histrova, Gloria Pérez-Guerrero, Adrián Pérez-Aranda, Selene Fernández-Martínez, Alicia Monreal-Bartolomé, Alberto Barceló-Soler, Javier García-Campayo, Yolanda López Del Hoyo, Jesús Herrera-Imbroda, Jessica Marian Goodman-Casanova, Jose Guzman-Parra","doi":"10.2196/98624","DOIUrl":"10.2196/98624","url":null,"abstract":"<p><strong>Background: </strong>Health care professionals are exposed to high levels of occupational stress, emotional exhaustion, and anxiety-depressive symptoms. Digital interventions using mindfulness- and acceptance-based approaches have shown promising results in improving psychological well-being in this population. The MINDxYOU program is a self-guided digital intervention designed to reduce perceived stress and enhance emotional regulation, resilience, and psychological flexibility. Although its effectiveness has been demonstrated, treatment response may vary substantially between individuals.</p><p><strong>Objective: </strong>The aims of this study are to explore the sociodemographic, occupational, and psychological moderators of the effectiveness of the digital MINDxYOU program on perceived stress among health care professionals and to identify differential response profiles.</p><p><strong>Methods: </strong>This study is a secondary analysis of a stepped-wedge cluster randomized trial including 347 health care professionals from 6 clusters allocated to 3 intervention start sequences. The intervention (MINDxYOU) is a self-guided, web-based program designed to be completed over 8 weeks, grounded in third-wave psychological approaches and incorporating mindfulness-, compassion-, and acceptance-based components. Weekly support was provided via WhatsApp Messenger (Meta Platforms Inc), phone calls, or email to promote adherence. Assessments were conducted every 8 weeks across 5 measurement waves. Linear mixed-effects models were used to evaluate moderation effects by testing condition × moderator interactions, accounting for repeated measures within participants and clustering effects.</p><p><strong>Results: </strong>Moderation analyses showed greater reductions in perceived stress among participants with higher baseline depressive symptom scores (9-item Patient Health Questionnaire [PHQ-9]; B=-0.22, SE 0.08; P=.004) and those with higher scores in the observing facet of mindfulness (15-item Five Facet Mindfulness Questionnaire [FFMQ-15]; B=-0.86, SE 0.41; P=.04). In addition, nursing staff exhibited greater reductions in perceived stress compared with physicians (B=1.92, SE 0.81; P=.02) and other health care professionals (B=1.80, SE 0.90; P=.04).</p><p><strong>Conclusions: </strong>The results suggest that baseline psychological and occupational characteristics may contribute to differences in the effectiveness of the MINDxYOU program in reducing perceived stress. These findings provide preliminary evidence regarding potential response profiles in digital mental health interventions and support the development of more personalized and targeted approaches to stress management in health care settings.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e98624"},"PeriodicalIF":5.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13476002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148763166","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}