Nevin Manimala

Nurse Educ Pract. 2025 Jun 20;86:104436. doi: 10.1016/j.nepr.2025.104436. Online ahead of print.

ABSTRACT

BACKGROUND: Accidental injuries, especially thoracic trauma, are a leading cause of death worldwide. Nurses need systematic training to improve trauma care competency. Traditional approaches limit instructor involvement and learner participation. Integrating online simulation with decision-branch may enhance clinical reasoning and improve learning outcomes.

OBJECTIVE: This study aimed to evaluate the effectiveness of an online simulation-based learning approach with decision-branching videos (Online SimuBranch) in enhancing nurses’ knowledge of thoracic trauma care and clinical reasoning ability.

DESIGN: Randomized controlled trial with two-group repeated measures design was used.

METHODS: A convenience sample of 95 nurses from a regional hospital in Taiwan was randomly assigned to either experimental (n = 49). or control group (n = 46). The experimental group received a thoracic trauma care course using Online SimuBranch, while the control group received traditional lecture-based instruction. Data were collected at pre-intervention, one-week post-intervention and twelve-week post-intervention. Instruments used were a demographic information sheet, Thoracic Trauma Knowledge Scale and Clinical Reasoning Ability Scale. Results were analyzed using Generalized Estimating Equations (GEE).

RESULTS: The experimental group showed a significant improvement in thoracic trauma knowledge (p < .001), sustained for twelve weeks. Their clinical reasoning scores were higher than the control group but not statistically significant (p > .05).

CONCLUSION: This study found that the Online SimuBranch enhances thoracic trauma knowledge. Although not statistically significant, the intervention enhanced nurses’ reasoning ability and confidence in trauma care. Future initiatives are recommended to incorporate Online SimuBranch into continuous education to strengthen the connection between knowledge and practice.

PMID:40561561 | DOI:10.1016/j.nepr.2025.104436

JMIR Form Res. 2025 Jun 25;9:e60859. doi: 10.2196/60859.

ABSTRACT

BACKGROUND: Given the recent evolution and achievements in brain-computer interface (BCI) technologies, understanding public perception and sentiments toward such novel technologies is important for guiding their communication strategies in marketing and education.

OBJECTIVE: This study aims to explore the public perception of BCI technology by examining posts on X (formerly known as Twitter) using natural language processing (NLP) methods.

METHODS: A mixed methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,962 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We used the Sentiment.ai tool to infer users’ demographics by matching predefined attributes in users’ profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.

RESULTS: The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk’s announcement of Neuralink. Sentiment analysis revealed that 59.38% (38,804/65,340) of posts were neutral, 32.75% (21,404/65,340) were positive, and 7.85% (5132/65,340) were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic=0.266; τ=0.266; P<.001). Most posts were objective (50,847/65,340, 77.81%), with a smaller proportion being subjective (14,393/65,340, 22.02%). Biographic analysis showed that the “broadcasting” group contributed the most to BCI discussions (17,803/58,030, 30.67%), while the “scientific” group, contributing 27.58% (n=16,005), had the highest overall engagement metrics. The emotional analysis identified anticipation (score = 10,802/52,618, 20.52%), trust (score=9244/52,618, 17.56%), and fear (score=7344/52,618, 13.95%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification.

CONCLUSIONS: This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy making, and communication strategies.

PMID:40561510 | DOI:10.2196/60859

JMIR Public Health Surveill. 2025 Jun 25;11:e65573. doi: 10.2196/65573.

ABSTRACT

BACKGROUND: To complete the Ending the HIV Epidemic initiative in areas with high HIV incidence, there needs to be a greater understanding of the demographic, behavioral, and geographic factors that influence the rate of new HIV diagnoses. This information will aid the creation of targeted prevention and intervention efforts.

OBJECTIVE: This study aims to identify the geographic distribution of risk groups and their role within potential transmission networks in Florida.

METHODS: Public data from the Florida Department of Health and behavioral data from the Surveillance Tools and Reporting System between 2012 and 2022 were used in these analyses. We analyzed records as a combination of variables of interest (gender, age, race or ethnicity, and HIV risk group) to create demographic-behavioral profiles (DBPs) that represent the profiles of people newly diagnosed with HIV. We then used the resulting DBPs to characterize Florida counties and HIV coordination areas and calculated the county-to-county and area-to-area rank (Spearman) correlation. We then drew a dendrogram based on the correlation matrix and identified clusters of similar counties and areas. Lastly, network analysis used HIV contact tracing data from the Surveillance Tools and Reporting System to identify HIV transmission and exposure contact networks and characterized large networks by DBPs and geolocation.

RESULTS: We identified 37 DBPs. The largest DBPs were Hispanic and non-Hispanic Black males aged 25-49 reporting male-to-male sexual contact (n=7539 and n=4329, respectively), non-Hispanic White males aged 25-49 reporting male-to-male sexual contact (n=4221), and non-Hispanic Black females aged 25-49 reporting heterosexual contact (n=3371). The state could be broken up generally into 2 transmission and exposure clusters by region: Northwestern or Northern and Central or Southern. We identified several counties with similar DBPs that were not in the same HIV coordination area. A total of 3097 contact networks were identified among 7944 people with HIV contact tracing data. Most (n=2508, 81%) networks involve only 2 people, 11% (n=349) involve 3 people, 7% (n=224) involve 4 to 19 people, and 6 networks involve 20 or more people. As network size increases, the proportion of people within the network who identify as female, non-Hispanic Black, aged older than 50 years, and exposed to HIV via heterosexual contact decreases.

CONCLUSIONS: We identified distinct risk groups and clusters of transmission and exposure throughout Florida. These results can help regions identify health disparities and allocate their HIV prevention and intervention resources accordingly. The goal of this work was to highlight areas of need in a high-incidence setting, not to contribute to existing stigma against vulnerable groups, and it is important to consider the ethics and possible harm of advanced methodologies such as contact network analysis when addressing public health problems.

PMID:40561506 | DOI:10.2196/65573

JMIR Mhealth Uhealth. 2025 Jun 25;13:e59926. doi: 10.2196/59926.

ABSTRACT

BACKGROUND: With a focus on postoperative dysfunctions that may occur after maxillofacial tumor resection and the difficulties faced during home rehabilitation, we developed a mobile health app based on nurse-patient cooperation. The app extends rehabilitation care from hospital to home with the help of artificial intelligence, Internet of Things, and other technologies, thus helping patients to better carry out their home functional rehabilitation and meet their health needs.

OBJECTIVE: The primary objective of this quasi-experimental study is to evaluate the impact of the Intelligent Home Rehabilitation Care Platform on the quality of life, self-management, and functional impairment in patients with oral and maxillofacial head and neck tumors. We aim to determine whether the intervention through this platform can lead to significant improvements in these areas compared with traditional postdischarge care methods.

METHODS: In this study, patients with oral and maxillofacial head and neck tumors who had undergone surgery were recruited from allied hospitals in the Yangtze River Delta region, divided into an experimental group (n=138) and a control group (n=123), and received either the Intelligent Home Rehabilitation Care Platform intervention or the conventional health care and guidance, respectively. The intervention lasted 3 months, and the patients’ quality of life, self-management efficacy, and improvement in dysfunction were assessed at 1 week (baseline), 1 month (T1), and 3 months (T2) postoperatively. SPSS software (IBM Corp) was used to perform the chi-square test, rank sum test, t test, repeated-measures ANOVA, and generalized estimation equation for data analysis.

RESULTS: We analyzed the effects of the quality of life and self-management using the generalized estimating equation method. The generalized estimating equation results showed that after adjusting for age, sex, pathological histology, cancer stage, and primary site, the intervention group had a significantly higher improvement in quality of life than the control group at the T2 (regression coefficient, β=-68.020, 95 % CI -116.639 to -19.412; P=.006) stage. The degree of improvement in self-management efficacy was significantly higher in the T1 (regression coefficient, β =-7.030, 95 % CI -9.540 to -4.520; P<.001) and T2 (regression coefficient, β =-13.245, 95 % CI -16.923 to -9.566; P<.001) stages than in the control group. The results of repeated-measures ANOVA and rank sum test showed that the experimental group showed improvements in shoulder function, dysphagia, and trismus after mHealth intervention; however, the differences were not significant.

CONCLUSIONS: The Intelligent Home Rehabilitation Care Platform interventions can effectively enhance patients’ self-management efficacy, improving quality of life and facilitate recovery from dysfunctions. Therefore, mHealth may be used in oncology care to provide smarter home self-care for patients.

PMID:40561502 | DOI:10.2196/59926

JMIR Cardio. 2025 Jun 25;9:e71366. doi: 10.2196/71366.

ABSTRACT

BACKGROUND: Advances in digital technology, such as health apps and telerehabilitation systems, offer promising treatment modalities in the secondary prevention of cardiovascular disease. However, the successful adoption of digital technology in clinical practice depends on a variety of factors. A comprehensive understanding of the influencing factors on digital technology usage in health care can support the complex implementation process of digital technology in clinical practice.

OBJECTIVE: The aim of this study was to identify barriers and facilitators of digital technology usage in cardiovascular disease secondary prevention from the perspective of health care professionals, and to explore whether certain characteristics of health care professionals are related to the current usage of digital technology in clinical practice.

METHODS: We conducted an exploratory online survey, inquiring about the perspectives and uses of digital technologies in cardiovascular disease secondary prevention. We developed an original questionnaire to address the study aim. The survey invitation was distributed among health care professionals from November 2021 to February 2022, via all cardiac rehabilitation centers, all community-based disease management services for patients with chronic heart failure, and all relevant national health care professional associations in Austria. Qualitative survey data were analyzed using thematic content analysis. Quantitative survey data were analyzed using descriptive statistics, group comparison tests, and association statistics.

RESULTS: Overall, 125 health care professionals (mean age 41, SD 11 y; n=80, 64% females) across different professions and settings, including cardiac rehabilitation phases I through IV, were recruited. General readiness for using digital technologies in the care of cardiac patients was high, but only 65 (52%) respondents reported doing so. The top 3 rated barriers to digital technology use were poor user-experience of devices and apps, lack of cost coverage, and low digital competence of patients. The top 3 rated potential application areas for digital technology were organization and appointment planning, documenting treatments, and creating personalized treatment plans. The top 3 rated facilitators for digital technology use were assurance of patient safety, assurance of patients’ privacy, and availability of technical support. Greater personal use of digital technology, younger age, and higher technology affinity of health care professionals was associated with higher readiness to use digital technology with cardiac patients.

CONCLUSIONS: While there is interest in digital technology for the secondary prevention of cardiovascular disease in Austria, barriers to uptake need to be addressed. Our findings may inform the design and implementation of future digitalization projects.

PMID:40561497 | DOI:10.2196/71366

JMIR Form Res. 2025 Jun 25;9:e74043. doi: 10.2196/74043.

ABSTRACT

BACKGROUND: Despite national asthma care guidelines, care gaps persist between best-practice and clinical practice, contributing to poor health outcomes. The Provider Asthma Assessment Form (PAAF) is an electronic asthma management and Knowledge Translation tool with an embedded decision support algorithm for severe and/or uncontrolled asthma, designed to support evidence-based asthma management.

OBJECTIVE: In this study, we aimed to document baseline asthma practice patterns and determine whether the broader intervention of PAAF integration into a primary care electronic medical record (EMR) improves evidence-based asthma diagnosis and management.

METHODS: We performed a single-center pre- and postobservational study at an academic Family Health Team in Kingston, Ontario, Canada. Retrospective baseline data were collected for 2 years prior to PAAF implementation from January 2018 to December 2019. Prospective postintervention data were collected from October 2022 to July 2024. A validated adult asthma EMR case definition was applied to EMR data to identify suspected or objectively confirmed asthma cases for both datasets, on which detailed manual chart abstractions were performed. A data extraction was performed for completed PAAFs.

RESULTS: There were 230 patients in the retrospective baseline and 143 patients in the postimplementation cohort. Overall, 31.3% (n=72) of patients at baseline versus 23.8% (n=34) at postimplementation had confirmed asthma. There were significantly more pulmonary function tests requested after the implementation of the PAAF (postimplementation: n=70, 49%; baseline: n=71, 30.9%; P<.001). A significantly higher percent of postimplementation patients were on single inhaler controller and reliever therapy (postimplementation: n=31, 21.7%; baseline: n=2, 0.9%; P<.001), inhaled corticosteroid/long-acting β-2 agonist therapy (postimplementation: n=36, 25.2%; baseline: n=34, 14.8%; P=.01), and inhaled corticosteroid if their asthma was uncontrolled (postimplementation: n=69, 62.2%; baseline: n=100, 43.5%; P=.002). Barriers were significantly more commonly addressed after implementation (postimplementation: n=24, 16.8%; baseline: n=11, 4.8%; P<.001). A significantly higher average number of asthma control parameters was documented when the PAAF was used (PAAF: mean 5.4, SD 1.9; manual chart abstraction: mean 2.3, SD 1.2; P<.001). Care as assessed by key Primary Care-Asthma Performance Indicators showed improvement in the postimplementation cohort, which did not reach statistical significance.

CONCLUSIONS: The multifaceted intervention of implementing the PAAF in this primary care practice was associated with improved documentation of diagnosis status and asthma control parameters and improved adherence with evidence-based recommendations for care, such as the use of pulmonary function tests and addressing barriers to effective asthma management. However, uptake was low, and key asthma care gaps were still common. Future directions should involve evaluating the impact of the PAAF on care and outcomes after widespread implementation in primary care settings and investigating methods to increase user uptake of the PAAF.

PMID:40561494 | DOI:10.2196/74043

Ter Arkh. 2025 Jun 8;97(5):463-470. doi: 10.26442/00403660.2025.05.203250.

ABSTRACT

AIM: To systematize data on the trends of the prevalence of Helicobacter pylori infection among the Moscow adult population.

MATERIALS AND METHODS: The MEDLINE/PubMed, EMBASE, Google Scholar, and the Russian Science Citation Index were searched for studies published between January 1, 1985, and February 7, 2025, inclusive. Publications were selected based on an analysis of their titles and abstracts. Included were peer-reviewed publications in Russian and English containing data on the prevalence of H. pylori among the adult population of Moscow, studies using validated methods for the diagnosis of H. pylori based on serological, urease respiratory tests, histological and molecular methods, as well as publications containing detailed statistical processing of data suitable for inclusion in the meta-analysis.

RESULTS: The final analysis included seven studies totaling 7,581 subjects (the overall mean age of all subjects was 48.28±13.20 years). The overall prevalence of H. pylori infection in the adult population of Moscow in the analyzed pool of studies for 18 years (2006-2024) was 66.534% (95% confidence interval [CI] 42.097-86.989), including 78.661% (95% CI 59.400-92.910) when using serological diagnostic methods and 48.473% (95% CI 32.331-64.781) when using the ¹³C-urease respiratory test. Studies conducted before 2015 showed the prevalence of H. pylori infection of 81.294% (95% CI 67.202-92.109), 68.028% (95% CI 29.383-95.895) in 2015-2020, and 39.860% (95% CI 33.993-45.877) after 2020. The meta-regression analysis revealed a statistically significant decrease in the prevalence of H. pylori depending on the year of the study (regression coefficient for the year -4.22 (95% confidence interval -6.27–2.17; p<0,0099).

CONCLUSION: The meta-analysis showed a gradual regression in the prevalence of H. pylori infection in Moscow, the largest metropolis in Russia and Europe. However, infection prevalence in the adult population remains relatively high, supporting the need for continued programs of timely diagnosis of H. pylori and subsequent eradication therapy to reduce the risk of associated diseases.

PMID:40561491 | DOI:10.26442/00403660.2025.05.203250

Ter Arkh. 2025 Jun 8;97(5):434-442. doi: 10.26442/00403660.2025.05.203228.

ABSTRACT

BACKGROUND: Osteoarthritis (OA) is a common joint disease and one of the leading causes of disability worldwide. The role of vitamin D in the etiology and development of OA is still unclear, but it may be important for both diagnosis and timely therapy.

AIM: To evaluate the relationship of vitamin D levels with clinical and instrumental parameters in OA in a cross-sectional study.

MATERIALS AND METHODS: The study included 171 patients aged 40-75 with confirmed knee OA according to the American College of Rheumatology (ACR) classification, stage I-III (according to Kellgren and Lawrence). All patients signed informed consent. The mean age was 53.5±9.94 years, body mass index (BMI) was 29.8±6.4 kg/m², and disease duration was 3 [1; 7] years. For each patient, a case record form was filled out, including anthropometric indicators, medical history, clinical examination data, an assessment of knee joint pain according to the Visual Analog Scale (VAS), WOMAC, and the patient’s general health condition (PGHC). All patients underwent standard radiography, knee ultrasound examination and magnetic resonance imaging (MRI) (WORMS), densitometry of the lumbar spine and femoral neck, and laboratory tests. Statistical processing of the data was performed using the Statistica 10 software.

RESULTS: Normal vitamin D values (≥30 ng/mL) were found in 62 (36.3%) patients, low levels (<30 ng/mL) in 109 (63.7%) patients, insufficiency (<30 ng/mL and >20 ng/mL) in 66 (38.6%) patients, and deficiency (<20 ng/mL) in 43 (25.1%). Patients were divided into three groups according to the presence or absence of vitamin D insufficiency/deficiency: Group 1 included patients with normal vitamin D levels, Group 2 included patients with insufficiency, and Group 3 included patients with vitamin D deficiency. Patients of the three groups were comparable in age and disease duration but differed significantly in body weight, BMI, and waist measurement (higher in groups with reduced vitamin D values; p<0.05). Also, these patients had significantly higher VAS pain scores, total WOMAC and its components (pain, stiffness, and dysfunction), PGHC, and worse KOOS. More patients in Groups 2 and 3 had OA of the hip and hand joints, clinically detected synovitis, flat feet, and quadriceps muscle hypotrophy. Ultrasound examination significantly more often revealed a reduction of cartilage tissue on both the anteromedial and anterolateral surfaces of the knee joint; MRI showed more often osteitis in the medial condyles of the femur and tibia (p<0.05 for all values).

CONCLUSION: Our study demonstrated that low blood vitamin D levels (insufficiency/deficiency) were associated with a more severe knee OA. These patients had a large body weight, BMI, higher VAS pain values, WOMAC index (overall and its components), worse KOOS, PGHC, and smaller cartilage sizes in the medial parts of the knee joint (according to ultrasound); such patients were significantly more likely to have osteitis in the medial parts of the femur and tibia according to MRI. Also, stage II and III knee OA and OA of other localizations, clinically detected synovitis, quadriceps hypotrophy, and flat feet were more common.

PMID:40561487 | DOI:10.26442/00403660.2025.05.203228

JMIR Infodemiology. 2025 Jun 25;5:e65835. doi: 10.2196/65835.

ABSTRACT

BACKGROUND: There has been an increase in the prevalence of noncommunicable diseases in Malaysia. This can be prevented and managed through the adoption of healthy lifestyle behaviors, including not smoking, avoiding alcohol consumption, maintaining a balanced diet, and being physically active. The growing importance of using social media to deliver information on healthy behaviors has led health care professionals (HCPs) to lead these efforts. To ensure effective delivery of information on healthy lifestyle behaviors, HCPs should begin by understanding users’ current opinions about these behaviors and whether the users are receptive to recommended health practices. Nevertheless, there has been limited research conducted in Malaysia that aims to identify the sentiments and content of posts, as well as how well users’ perceptions align with recommended health practices.

OBJECTIVE: This study aims to examine social media posts related to various lifestyle behaviors, by using a combination of sentiment analysis to analyze users’ sentiments and manual content analysis to explore the content of the posts and how well users’ perceptions align with recommended health practices.

METHODS: Using keywords based on lifestyle behaviors, posts originating from X (formerly known as Twitter) and published in Malaysia between November and December 2022 were scraped for sentiment analysis. Posts with positive and negative sentiments were randomly selected for content analysis. A codebook was developed to code the selected posts according to content and alignment of users’ perceptions with recommended health practices.

RESULTS: A total of 3320 posts were selected for sentiment analysis. Significant associations were observed between sentiment class and lifestyle behaviors (χ26=67.64; P<.001), with positive sentiments higher than negative sentiments for all lifestyle behaviors. Findings from content analysis of 1328 posts revealed that most of the posts were about users’ narratives (492/1328), general statements (203/1328), and planned actions toward the conduct of their behavior (196/1328). More than half of tobacco-, diet-, and activity-related posts were aligned with recommended health practices, whereas most of the alcohol-related posts were not aligned with recommended health practices (63/112).

CONCLUSIONS: As most of the alcohol-related posts did not align with recommended health practices, the findings reflect a need for HCPs to increase their delivery of health information on alcohol consumption. It is also important to ensure the ongoing health promotion of the other 3 lifestyle behaviors on social media, while continuing to monitor the discussions made by social media users.

PMID:40561482 | DOI:10.2196/65835

JMIR Res Protoc. 2025 Jun 25;14:e68517. doi: 10.2196/68517.

ABSTRACT

BACKGROUND: Suicide in local jails occurs at a higher rate than in the general population, requiring improvements to risk screening methods. Current suicide risk screening practices in jails are insufficient: They are commonly not conducted using validated screening instruments, not collected by clinically trained professionals, and unlikely to capture honest responses due to the chaotic nature of booking areas. Therefore, new technologies could improve such practices. Several studies have indicated that machine learning (ML) models considerably improve accuracy and have positive predictive value in detecting suicide risk compared with practice as usual (PAU). This study will use administrative data and ML modeling to improve suicide risk detection at jail booking.

OBJECTIVE: This study is primarily focused on gathering preliminary information about the feasibility and practicality of using administrative data and ML modeling for suicide risk detection but also incorporates elements of hypothesis testing pertaining to clinical outcomes.

METHODS: The study uniquely contributes to our understanding of suicide risk by further validating an existing ML model developed and previously validated by the Mental Health Research Network using Medicaid outpatient health care claims data. This validation uses complete claims data on a sample of approximately 6000 individuals booked into 2 diverse jails in a midwestern state. This model validation uses 313 unique demographic and clinical characteristics from 5 years of historical health care data. It detects suicide risk in jails and postrelease by using merged jail, Medicaid, and vital records data. The study will use jail administrative data for September 1, 2021, through February 28, 2022; Medicaid records data for September 1, 2016, through March 31, 2023; and vital records data for March 1, 2022, through March 31, 2023.

RESULTS: First, the algorithm will be validated on the data gathered for the jail sample using the C-statistic and area under the receiver operating characteristic curve. Second, the resulting model will be compared with the jails’ suicide identification PAU to assess risk and detection of identified suicide attempts and deaths from intake through 120 days and 13 months after jail release. The funding timeline for this project is August 1, 2022, through July 31, 2025. The algorithm’s predictions and actual event incidence will be linked and validated in the spring of 2025, with results ready for publication in the fall of 2025.

CONCLUSIONS: The study will also investigate implementation factors, such as feasibility, acceptability, and appropriateness, to optimize jail uptake. Interview data on the implementation factors will be gathered in the summer of 2025, with expected dissemination in 2026. We hypothesize that a combination of intake screening PAU and the ML model will be the optimal approach, in that the combination will be more accurate and can have practical application in this context.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/68517.

PMID:40561472 | DOI:10.2196/68517