Tag: pubmed

Int J Behav Nutr Phys Act. 2025 Mar 3;22(1):25. doi: 10.1186/s12966-025-01716-9.

ABSTRACT

BACKGROUND: The time spent physically active outside of school (e.g., extracurricular physical activity) is an important contributor to children’s total daily physical activity for health and well-being. Little is known about the opportunities available to children to engage in extracurricular physical activity from low- to middle-income countries. This study aims to answer the question: What are the main perceived barriers and facilitators of extracurricular physical activity among school-age children in Mexico?

METHODS: A multi-method cross-sectional study was performed. Six focus groups with children (aged 9-12 years), six focus groups with parents, 10 one-on-one interviews with parents, 12 interviews with teachers, and six interviews with head teachers were conducted across Campeche, Morelos, and Mexico State, Mexico. A questionnaire was applied to explore children’s physical activity frequency and preferences for time inside and outside of school. Qualitative data analyses were performed with inductive thematic analysis supported with NVivo software. Quantitative data were analysed with descriptive statistics using IBM SPSS 26.

RESULTS: Three main themes summarise the study’s findings: (1) how children spend their time outside of school, (2) the places that children access, and (3) the social environment for physical activity outside of the school. The data suggest that children in Mexico dedicate their spare time to screen, work, do housework, or perform unstructured physical activity mostly at home instead of playing sports or actively outdoors. Family support, enjoyment of physical activity, access to programs and facilities, time, living in a housing complex with open common areas, and mild weather were important facilitators identified. 69.4% of children engage in extracurricular physical activity, none of which was provided by schools. More children commute by walking than riding a bike to and from school. Children living inland spent three times more time at home compared to those in seafront areas.

CONCLUSIONS: Children rely on their families to partake in extracurricular structured physical activity. Policies targeting children’s health and well-being should include school-based extracurricular physical activity programs.

PMID:40033367 | DOI:10.1186/s12966-025-01716-9

BMC Health Serv Res. 2025 Mar 4;25(1):330. doi: 10.1186/s12913-025-12456-8.

ABSTRACT

BACKGROUND: To enable people with stroke to achieve an active everyday life under altered conditions, the development of self-management programs is essential to facilitate the process of change that individuals must undergo. To improve access to self-management, internet-based solutions have been proposed. The aim of this study was to evaluate the feasibility of a novel internet-based intervention, “Strategies for Empowering activities in Everyday Life” (SEE, version 1.0), for clients with stroke.

METHODS: This feasibility study had a preposttest design without a control group and utilized a mixed-method approach. Data were collected through study-specific forms, outcome assessments, interviews, and field notes. Descriptive statistics and content analysis were subsequently applied.

RESULTS: The study involved fifteen clients and staff at clinics in a hospital-based open-care rehabilitation setting. The results indicate that SEE is feasible for clients with stroke. When adopted as expected, SEE has the potential to empower self-management and enhance engagement, balance, and values in everyday activities. The study also indicates that SEE is feasible in terms of adherent delivery of dosage, acceptability, and value, as perceived by clients, occupational therapists, and clinic managers. However, adjustments are needed in the study design, in terms of recruitment strategies, the selection of assessor-based outcome assessment, and the evaluation of adherence. Additionally, the educational program for professionals should be enhanced to better support the implementation of SEE.

CONCLUSION: After the study design, intervention, and educational program are refined, SEE can be prepared for a pilot randomized controlled trial.

TRIAL REGISTRATION: clinicaltrails.gov NCT04588116, date of registration: 8th October 2020.

PMID:40033363 | DOI:10.1186/s12913-025-12456-8

BMC Nurs. 2025 Mar 3;24(1):236. doi: 10.1186/s12912-025-02839-x.

ABSTRACT

BACKGROUND: Needle-stick injuries (NSIs) pose a substantial occupational hazard, exposing healthcare professionals to potentially infectious diseases. Nurses’ clinical competence plays a crucial role in preventing and mitigating the incidence of NSIs. This study aimed to investigate the relationship between clinical competence and the incidence of NSIs, as well as the factors contributing to these injuries, among nurses in Fars Province, southern Iran, from March 2023 to May 2023.

METHODS: This descriptive cross-sectional study included 264 nurses selected through convenience sampling. All participants were employed in various departments of teaching hospitals in Fasa city, southern Iran, during the study period. Data were collected using a demographic questionnaire and a clinical competence questionnaire specifically developed for nurses. The demographic questionnaire captured variables such as age, gender, marital status, educational background, departmental assignment, work experience, and weekly working hours. The clinical competence questionnaire consisted of 55 items assessing seven dimensions: clinical care, leadership, legal and ethical performance, professional development, interpersonal relationships, education and coaching, and critical thinking and research aptitude. Statistical analyses were performed using SPSS software (version 16), employing the Chi-square test, Kruskal-Wallis test, and multiple logistic regression analysis. A significance level of p < 0.05 was applied to all tests.

RESULTS: The findings revealed that 39.4% of the participating nurses exhibited high clinical competence, 51.5% demonstrated moderate competence, and 9.1% were classified as having low competence. Statistical analysis indicated a significant association between clinical competence levels and needle-stick status (P = 0.002). Moreover, a significant difference was identified between clinical competence levels and the frequency of NSIs (P = 0.001). A logistic regression model was employed to assess the likelihood of NSIs based on demographic variables. The results showed that 178 participants (67.42%) had experienced needle-stick or sharp injuries within the preceding year. Among these, 63 males (35.3%) and 115 females (64.6%) reported such incidents. The highest incidence of needle-stick and sharp injuries occurred in the Operating Room (91.7%), followed by Dialysis (88.9%), Pediatrics (80%), Surgical Intensive Care (76.5%), Emergency (74.3%), Women’s Surgery (70%), Post-Cardiac Intensive Care (69.2%), Oncology (63.6%), Internal Medicine (59.1%), Surgery and Infectious Diseases (54.5%), Laboratory and Cardiac Intensive Care (52.9%), Men’s Surgery (50%), and the Psychiatric Ward (41.2%).

CONCLUSIONS: Considering that the majority of nurses working in hospitals exhibited moderate to low levels of clinical competence, it is recommended that hospitals implement an annual clinical competence assessment for nurses. Regular evaluations and targeted training programs can enhance nurses’ competence levels, thereby improving patient care quality and reducing the incidence of NSIs among healthcare providers. Additionally, specific strategies should be developed and implemented in medical centers and hospitals to mitigate the risk of NSIs across all hospital departments, particularly in high-risk areas such as operating rooms and dialysis units, where the prevalence of NSIs is significantly higher.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40033355 | DOI:10.1186/s12912-025-02839-x

BMC Med Inform Decis Mak. 2025 Mar 3;25(1):105. doi: 10.1186/s12911-025-02937-5.

ABSTRACT

Current research on the association between demographic variables and dietary patterns with atherosclerotic cardiovascular disease (ASCVD) is limited in breadth and depth. This study aimed to construct a machine learning (ML) algorithm that can accurately and transparently establish correlations between demographic variables, dietary habits, and ASCVD. The dataset used in this research originates from the United States National Health and Nutrition Examination Survey (U.S. NHANES) spanning 1999-2018. Five ML models were developed to predict ASCVD, and the best-performing model was selected for further analysis. The study included 40,298 participants. Using 20 population characteristics, the eXtreme Gradient Boosting (XGBoost) model demonstrated high performance, achieving an area under the curve value of 0.8143 and an accuracy of 88.4%. The model showed a positive correlation between male sex and ASCVD risk, while age and smoking also exhibited positive associations with ASCVD risk. Dairy product intake exhibited a negative correlation, while a lower intake of refined grains did not reduce the risk of ASCVD. Additionally, the poverty income ratio and calorie intake exhibited non-linear associations with the disease. The XGBoost model demonstrated significant efficacy, and precision in determining the relationship between the demographic characteristics and dietary intake of participants in the U.S. NHANES 1999-2018 dataset and ASCVD.

PMID:40033349 | DOI:10.1186/s12911-025-02937-5

Scand J Trauma Resusc Emerg Med. 2025 Mar 3;33(1):35. doi: 10.1186/s13049-025-01350-1.

ABSTRACT

BACKGROUND: For preoxygenation, German guidelines consider non-invasive ventilation (NIV) as a possible method in prehospital trauma care in the absence of aspiration, severe head or face injuries, unconsciousness, or patient non-compliance. As data on the utilization and characteristics of patients receiving NIV are lacking, this study aims to identify predictors of NIV usage in trauma patients using machine learning and compare these findings with the current national guideline.

METHODS: A cross-regional registry of prehospital emergency services in southwestern Germany was searched for cases of emergency anesthesia in multiply injured patients in the period from 2018 to 2020. Initial vital signs, oxygen saturation, respiratory rate, heart rate, systolic blood pressure, Glasgow Coma Scale (GCS), injury pattern, shock index and age were examined using logistic regression. A decision tree algorithm was then applied in parallel to reduce the number of attributes, which were subsequently tested in several machine learning algorithms to predict the usage of NIV before the induction of anesthesia.

RESULTS: Of 992 patients with emergency anesthesia, 333 received NIV (34%). Attributes with a statistically significant influence (p < 0.05) in favour of NIV were bronchial spasm (odds ratio (OR) 119.75), dyspnea/cyanosis (OR 2.28), moderate and severe head injury (both OR 3.37) and the respiratory rate (OR 1.07). Main splitting points in the initial decision tree included auscultation (rhonchus and bronchial spasm), respiratory rate, heart rate, age, oxygen saturation and head injury with moderate head injury being more frequent in the NIV group (23% vs. 12%, p < 0.01). The rates of aspiration and the level of consciousness were equal in both groups (0.01% and median GCS 15, both p > 0.05). The prediction accuracy for NIV usage was high for all algorithms, except for multilayer perceptron and logistic regression. For instance, a Bayes Network yielded an AUC-ROC of 0.96 (95% CI, 0.95-0.96) and PRC-areas of 0.96 [0.96-0.96] for predicting and 0.95 [0.95-0.96] for excluding NIV usage.

CONCLUSIONS: Machine learning demonstrated an excellent categorizability of the cohort using only a few selected attributes. Injured patients without severe head injury who presented with dyspnea, cyanosis, or bronchial spasm were regularly preoxygenated with NIV, indicating a common prehospital practice. This usage appears to be in accordance with current German clinical guidelines. Further research should focus on other aspects of the decision making like airway anatomy and investigate the impact of preoxygenation with NIV in prehospital trauma care on relevant outcome parameters, as the current evidence level is limited.

PMID:40033329 | DOI:10.1186/s13049-025-01350-1

BMC Sports Sci Med Rehabil. 2025 Mar 3;17(1):34. doi: 10.1186/s13102-025-01088-4.

ABSTRACT

Performance analysis can provide coaches with a range of relevant information and support more informed decision-making. The objective of this research was to determine running performance (RP) within five-minute intervals when scoring and conceding goals in the UEFA Champions League (UCL). Matches from the UCL 2020/2021 season were analyzed, and relevant data were retrieved using the InStat Fitness semi-automatic video system. Statistical analysis employed one-way analysis of variance (ANOVA) for comparisons and partial eta squared (η2) to determine effect size. Team performance was determined by measuring total distance covered (TD) and high-intensity running (HIR) when the team scored a goal, conceded a goal, and when the score did not change. Our primary results indicated significant differences in three out of 20 five-minute intervals for the TD parameter and four out of 20 for HIR when teams scored goals. There were also significant differences in eight out of 20 intervals for TD and three out of 20 for HIR when teams conceded goals. In conclusion, significant goal concessions were observed during all the five-minute intervals in which teams substantially reduced their RP. From a practical point of view, coaches should be aware, especially in the context of the pacing strategy used, that team RP affects the scoreline directly and the match outcome indirectly.

PMID:40033321 | DOI:10.1186/s13102-025-01088-4

BMC Public Health. 2025 Mar 3;25(1):838. doi: 10.1186/s12889-025-22040-8.

ABSTRACT

BACKGROUND: Evidence on psychological outcomes of gaming disorder (GD) is still scarce. This study aimed to investigate the mediating role of sleep hygiene in the relationship between GD and psychological distress (depression and anxiety) among Iranian adolescents.

METHODS: This was a cross-sectional study among school students in Qazvin city, Iran. We administered GD, anxiety, and depression questionnaires in a paper-and-pencil format. GD was measured using the GD S4-SF scale, and anxiety and depression were evaluated using the DASS-21. We assessed sleep health as a mediator using the Sleep Hygiene Behaviors scale. Covariance-Based Structural Equation Modeling (CB-SEM) was employed for data analysis, accounting for sex and physical activity as the main confounders. Statistical significance was determined using various fit indices and confidence intervals.

RESULTS: The sample consisted of 600 adolescents (41% female). CB-SEM revealed a positive but not statistically significant association between GD and depression, along with a negative statistically significant association with anxiety. Notably, sleep hygiene was identified as a partial mediator in the relationship between GD and depression, indicating that poor sleep practices may exacerbate depressive symptoms among adolescents with GD. However, no mediating effect was observed for anxiety.

CONCLUSION: Our data supported a mediating role for sleep hygiene in the association between GD and depression among participants. Our results highlight the critical need for targeted policy interventions to improve sleep hygiene among adolescents with GD.

PMID:40033319 | DOI:10.1186/s12889-025-22040-8

BMC Musculoskelet Disord. 2025 Mar 3;26(1):217. doi: 10.1186/s12891-025-08389-2.

ABSTRACT

BACKGROUND: Osteoporosis is common in patients with knee osteoarthritis (KOA) awaiting total knee arthroplasty (TKA) and varies in different regional and ethnic. However, it is unclear whether the prevalence of osteoporosis and osteopenia in these patients is different from that in the general population. This study aims to investigate the prevalence of osteoporosis and osteopenia in both populations to help exploring the relationship between the osteoporosis and osteoarthritis, and to explore whether knee function and radiological assessments of KOA are associated with osteoporosis.

METHODS: In total, 249 patients diagnosed with KOA awaiting TKA were investigated in this cross-sectional study. The mean age was 70.9 ± 6.4 years. Bone mineral density (BMD) and T scores at the hip and lumbar spine were used to assess bone status using dual X-ray absorptiometry. A matched cohort from 2448 individuals in the Health Examination Center of our hospital was set as controls by matching sex, age (± 3.0 years) and BMI (± 1.0). The Kellgren-Lawrence grades (K-L grades), mechanical femorotibial angle (mFTA), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score and range of motion (ROM) of the knee were measured to evaluate radiological assessments and knee function in patients awaiting TKA and used to explore the association between KOA and BMD or T score. Prevalence of osteoporosis and osteopenia were investigated in the two cohorts, and inferential statistical analyses were undertaken. The chi-squared test or Fisher’s exact test was used for categorical variables while comparisons of scores were examined by ANOVA with/without Bonferroni correction or the Kruskal‒Wallis test.

RESULTS: The prevalence of osteoporosis and osteopenia in patients awaiting TKA was 30.5% (76/249) and 44.2% (110/249), respectively. In the matched cohort, 72/249 (28.9%) had osteoporosis, while 98/249 (39.4%) had osteopenia. There was no significant difference in the prevalence of osteoporosis or osteopenia between the two groups (χ2 = 2.603, P = 0.272). mFTA was significantly correlated with BMD and T score (P < 0.05), while no correlation was found between K-L grade, ROM or WOMAC and BMD or T score (P > 0.05).

CONCLUSIONS: The prevalence of osteoporosis in patients awaiting TKA was similar to that in the general population. BMD and T score were not correlated with WOMAC score or K-L grade but were correlated with mFTA.

PMID:40033308 | DOI:10.1186/s12891-025-08389-2

BMC Med Inform Decis Mak. 2025 Mar 3;25(1):109. doi: 10.1186/s12911-025-02945-5.

ABSTRACT

BACKGROUND: Algorithms and models increasingly support clinical and shared decision-making. However, they may be limited in effectiveness, accuracy, acceptance, and comprehensibility if they fail to consider patient preferences. Addressing this gap requires exploring methods to integrate patient preferences into model-based clinical decision-making.

OBJECTIVES: This scoping review aimed to identify and map applications of computational methods for incorporating patient preferences into individualized medical decision models and to report on the types of models where these methods are applied.

INCLUSION CRITERIA: This review includes articles without restriction on publication date or language, focusing on practical applications. It examines the integration of patient preferences in models for individualized clinical decision-making, drawing on diverse sources, including both white and gray literature, for comprehensive insights.

METHODS: Following the Joanna Briggs Institute (JBI) methodology, a comprehensive search was conducted across databases such as PubMed, Web of Science, ACM Digital Library, IEEE Xplore, Cochrane Library, OpenGrey, National Technical Reports Library, and the first 20 pages of Google Scholar. Keywords related to patient preferences, medical models, decision-making, and software tools guided the search strategy. Data extraction and analysis followed the JBI framework, with an explorative analysis.

RESULTS: From 7074 identified and 7023 screened articles, 45 publications on specific applications were reviewed, revealing significant heterogeneity in incorporating patient preferences into decision-making tools. Clinical applications primarily target neoplasms and circulatory diseases, using methods like Multi-Criteria Decision Analysis (MCDA) and statistical models, often combining approaches. Studies show that incorporating patient preferences can significantly impact treatment decisions, underscoring the need for shared and personalized decision-making.

CONCLUSION: This scoping review highlights a wide range of approaches for integrating patient preferences into medical decision models, underscoring a critical gap in the use of cohesive frameworks that could enhance consistency and clinician acceptance. While the flexibility of current methods supports tailored applications, the limited use of existing frameworks constrains their potential. This gap, coupled with minimal focus on clinician and patient engagement, hinders the real-world utility of these tools. Future research should prioritize co-design with clinicians, real-world testing, and impact evaluation to close this gap and improve patient-centered care.

PMID:40033306 | DOI:10.1186/s12911-025-02945-5

IEEE Trans Pattern Anal Mach Intell. 2025 Jan 9;PP. doi: 10.1109/TPAMI.2025.3527498. Online ahead of print.

ABSTRACT

Existing methods for integerized training speed up deep learning by using low-bitwidth integerized weights, activations, gradients, and optimizer buffers. However, they overlook the issue of full-precision latent weights, which consume excessive memory to accumulate gradient-based updates for optimizing the integerized weights. In this paper, we propose the first latent weight quantization schema for general integerized training, which minimizes quantization perturbation to training process via residual quantization with optimized dual quantizer. We leverage residual quantization to eliminate the correlation between latent weight and integerized weight for suppressing quantization noise. We further propose dual quantizer with optimal nonuniform codebook to avoid frozen weight and ensure statistically unbiased training trajectory as full-precision latent weight. The codebook is optimized to minimize the disturbance on weight update under importance guidance and achieved with a three-segment polyline approximation for hardware-friendly implementation. Extensive experiments show that the proposed schema allows integerized training with lowest 4-bit latent weight for various architectures including ResNets, MobileNetV2, and Transformers, and yields negligible performance loss in image classification and text generation. Furthermore, we successfully fine-tune Large Language Models with up to 13 billion parameters on one single GPU using the proposed schema.

PMID:40030978 | DOI:10.1109/TPAMI.2025.3527498