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To identify distinct social network types among young-old adults based on the characteristics of social network structure and to explore the relationship between different types, socio-demographic characteristics and subjective cognitive decline.
A cross-sectional study was conducted from July 2022 to October 2023.
A total of 652 young-old adults aged 60–74 years completed the sociodemographic questionnaire, the subjective cognitive decline questionnaire-9 and the self-designed egocentric social network questionnaire. The types of social networks were identified by latent profile analysis. Univariate analysis and binary logistic regression were used to analyse the influencing factors of subjective cognitive decline.
The incidence of subjective cognitive decline was 38%. Social networks of young-old adults tended to be large, predominantly family-centred and characterised by strong contact strength, high density and significant demographic heterogeneity among network members. Four social network types were identified: diverse-moderate, family-dense, family-strong and friend-loose. Young-old adults embedded in the family-dense and family-strong types were more likely to develop subjective cognitive decline than those in the diverse-moderate type. Additionally, age, education level, previous occupation, daily sleep duration and exercise were related to the incidence of subjective cognitive decline.
The findings highlight the relatively high incidence of subjective cognitive decline in young-old adults that is notably influenced by the type of social network they are embedded in. More attention needs to be paid to identifying and supporting young-old adults at high risk of subjective cognitive decline, especially to promote their social integration and friend network building, to improve their subjective cognitive function.
The findings emphasise the importance of considering the structure and composition of social networks when addressing subjective cognitive decline among young-old adults. A diversified social network incorporating both familial and friendship ties may provide enhanced cognitive protection. Therefore, interventions targeting subjective cognitive decline should promote the expansion of friendship-based relationships and foster the development of more heterogeneous and multi-source networks.
STROBE checklist.
Not applicable.
Post-chronic pancreatitis (CP) diabetes mellitus (PPDM-C) is a distinct form of diabetes, in which complex pathogenesis hampers adequate glycaemic control. This study aimed to identify risk factors for poor glycaemic status in PPDM-C to guide clinical management.
Cross-sectional study.
Shanghai, China.
Between January 2018 and March 2023, 1677 patients with CP were enrolled in the CP database of the National Clinical Research Center. After application of strict exclusion criteria, 302 patients diagnosed with PPDM-C were included in the study.
The primary outcome was glycaemic control. The secondary outcomes were factors that affect glycaemic control among patients with PPDM-C.
This retrospective study was conducted in patients with PPDM-C. Poor glycaemic status was defined as a glycated haemoglobin A1c level of >7% at admission. Patients were stratified into those with and without diabetes treatment. Multivariate logistic regression was performed to identify risk factors. The area under the curve (AUC) analysis was used to evaluate the predictive efficacy of these risk factors.
A total of 302 patients with PPDM-C were analysed. Poor glycaemic status was observed in 72.6% (61/84) of patients without diabetes treatment and 52.8% (115/218) of those with diabetes treatment. For those without diabetes treatment, a history of acute pancreatitis (AP) attacks (OR: 4.838, p=0.014) and smoking (1–20 pack-years, OR: 4.418; >20 pack-years, OR: 9.989; p0.001). In patients with diabetes treatment, AP attack history (OR: 5.640, p20 pack-years, OR: 11.395; p
Patients with PPDM-C in China exhibited a high prevalence of poor glycaemic status. Smoking and a history of AP attacks were significantly associated with an increased risk of poor glycaemic control. The early identification of patients with PPDM-C at elevated risk of poor glycaemic control may facilitate timely and optimised management of glycaemia.
This study was designed to explore the potential categories and their characteristics of self-compassion in Chinese enterostomy patients and then to investigate related factors.
A cross-sectional study.
The research focused on enterostomy patients who were hospitalised in two tertiary hospitals in Yangzhou City, China, between Nov 2022 and Aug 2023.
222 adult enterostomy patients in China completed the questionnaires.
This study investigated scores from the Self-Compassion Scale, Perceived Stress Scale and the Social Support Rating Scale. Information on the patients included: age, gender, marital status, monthly household income, types of medical insurance, education level, place of residence, enterostomy complications, postoperative time and whether adjuvant chemotherapy was given.
Three profiles of self-compassion in enterostomy patients were identified: ‘low self-compassion group’ (class 1), ‘moderate self-compassion group’ (class 2) and ‘high self-compassion group’ (class 3), accounting for 40.5%, 28.0% and 31.5%, respectively. The multivariate logistic analysis showed adjuvant chemotherapy, social support (PP
There is significant heterogeneity in self-compassion among enterostomy patients, and nearly half of them belong to the ‘low self-compassion group’. Focused interventions are required for females, patients with permanent enterostomy, low educational level and undergoing adjuvant radiotherapy. The self-compassion ability of patients can be effectively improved by reducing perceived stress and enhancing social support. These findings provide a basis for constructing targeted intervention strategies.
Stroke is a leading cause of death and disability worldwide, with spasticity affecting 4%–42.6% of stroke survivors. Prolonged spasticity can lead to pain, restricted joint mobility and muscle weakness. Current non-pharmacological treatments include physical therapy, orthoses and surgery. Muscle energy techniques (METs) and blood flow restriction training (BFRT) have shown promise in improving muscle function and reducing spasticity. This study aims to investigate the combined effect of MET and BFRT on upper limb motor function in patients with poststroke spasticity.
This study is a single-blind randomised controlled trial involving patients with poststroke spasticity. Participants will be randomly assigned to either the MET+BFRT group or the passive stretching group. Both groups will receive conventional rehabilitation therapy, with additional MET+BFRT or passive stretching interventions. The intervention will last for 6 weeks, with four sessions per week. Primary outcomes include the simplified Fugl-Meyer assessment (FMA) and surface electromyography, while secondary outcomes include the Modified Barthel Index and the Modified Ashworth Scale.
Based on literature data, patients who had a stroke have an average baseline upper limb FMA score of 40 points. Conventional rehabilitation typically improves FMA to 46 points (SD=8). This trial expects an additional 6-point improvement from the intervention. With α=0.05 (two-sided), 90% power (1–β=0.90) and 10% dropout rate, PASS V.11.0 calculation indicates a minimum requirement of 42 participants per group.
Statistical analysis will be conducted using IBM SPSS Statistics V.25. Intention-to-treat analysis will be used to analyse the result, which means the last observation will be used for interpolation when data are missing. Continuous variables will be summarised as mean±SD for normally distributed data or as median and IQRs for non-normally distributed data. Categorical variables will be presented as frequencies and percentages. For continuous variables that meet the criteria of normal distribution and homogeneity of variance, two-way analysis of variance with repeated measures will be applied; for those that do not meet these criteria, the Mann-Whitney U test will be used. Categorical variables will be analysed with the 2 test or Fisher’s exact test.
The study protocol has been approved by the ethics committee of Jiaxing Hospital of Traditional Chinese Medicine (2024-016). Participants will provide written informed consent before inclusion. The results will be disseminated through peer-reviewed journals and conference presentations.
ChiCTR2400085996.
This study aimed to (1) evaluate the effectiveness of e-health interventions in improving physical activity and associated health outcomes during pregnancy, (2) compare the e-health functions employed across interventions and (3) systematically identify the behaviour change techniques (BCTs) used and examine their interrelationships.
A systematic review and meta-analysis following the PRISMA 2020 guidelines.
Randomised controlled trials were included. Meta-analyses and subgroup analyses were performed using RevMan 5.3. Social network analysis was conducted to determine the most central BCTs within the intervention landscape.
Ten databases were searched, including PubMed, Embase, Web of Science, Cochrane Library, ProQuest, Scopus, SinoMed, China National Knowledge Infrastructure, WanFang and the China Science and Technology Journal Database, from inception to April 22, 2024.
Thirty-five studies were included. Pooled analyses indicated that e-health interventions significantly improved both total (SMD: 0.19; 95% CI: 0.10 to 0.27; I 2 = 55%) and moderate-to-vigorous physical activity (SMD: 0.16, 95% CI: 0.06 to 0.26; I 2 = 53%) in pregnant women. Subgroup analyses revealed that interventions based on theoretical frameworks and those not specifically targeting overweight or obese women demonstrated greater effectiveness. Additionally, e-health interventions were associated with significant reductions in both total and weekly gestational weight gain. Six of the twelve e-health functions were utilised, with ‘client education and behaviour change communication’ being the most prevalent. Thirty unique BCTs were identified; among them, ‘instruction on how to perform the behaviour’, ‘self-monitoring’, ‘problem solving’, and ‘goal setting’ showed the highest degree of interconnectedness.
E-health interventions are effective in enhancing physical activity and reducing gestational weight gain during pregnancy. Incorporating theoretical frameworks and well-integrated BCTs is recommended to optimise intervention outcomes.
Integrating e-health interventions into existing perinatal care models holds promise for enhancing physical activity among pregnant women and improving maternal health outcomes.
This study adhered to the PRISMA checklist.
No patient or public involvement.
The study protocol was preregistered in the International Prospective Register of Systematic Reviews (CRD42024518740)
by Hongfei Liu, Wenli Li, Gaoqiang Fan, Qiaoyi Chen, Shulei Zhang, Beibei Zhang
This study aimed to investigate the effects of dietary chitosan oligosaccharide (COS) supplementation on growth performance, antioxidant capacity, immune function, duodenal digestive enzyme activity, and jejunal morphology in growing female minks. Ninety-six 12-week-old minks were randomly assigned to six groups (0, 100, 200, 300, 400, or 500 mg/kg COS), with 8 replicates per treatment and 2 minks per replicate, for an 8-week trial. The results showed that average daily gain (ADG) increased quadratically with increasing COS levels (P P P P P P
by Mengqi Yuan, Yajing Yuan, Xiangqun Zhang, Zhenghao Zhu, Chenxi Zhao, Xiangqian Gao, Genyuan Du
Millimeter-wave (mmWave) radar has become an important research direction in the field of object detection because of its characteristics of all-time, low cost, strong privacy and not affected by harsh weather conditions. Therefore, the research on millimeter wave radar object detection is of great practical significance for applications in the field of intelligent security and transportation. However, in the multi-target detection scene, millimeter wave radar still faces some problems, such as unable to effectively distinguish multiple objects and poor performance of detection algorithm. Focusing on the above problems, a new target detection and classification framework of S2DB-mmWave YOLOv8n, based on deep learning, is proposed to realize more accuracy. There are three main improvements. First, a novel backbone network was designed by incorporating new convolutional layers and the Simplified Spatial Pyramid Pooling - Fast (SimSPPF) module to strengthen feature extraction. Second, a dynamic up-sampling technique was introduced to improve the model’s ability to recover fine details. Finally, a bidirectional feature pyramid network (BiFPN) was integrated to optimize feature fusion, leveraging a bidirectional information transfer mechanism and an adaptive feature selection strategy. A publicly available 5-class object mmWave radar heatmap dataset, including 2,500 annotated images, were selected for data modeling and method evaluation. The results show that the mean average precision (mAP), precision and recall of the S2DB-mmWave YOLOv8n model were 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% and 90.6%, respectively, which is 3.3, 1.6, 4.5 and 7.7 percentage points higher than the baseline YOLOv8n network without increasing the parameter count.
To explore how the mentor-student relationship affects nursing graduate students' satisfaction with mentors, as well as how mentoring mode and learning motivation work together.
A multi-centre cross-sectional study.
Thirty universities and colleges in eastern, central and western China.
A total of 826 nursing graduate students from thirty universities and colleges participated in this study in April 2024.
Data were collected using the general information questionnaire, mentor-student relationship entry, mentoring mode questionnaire, graduate students' satisfaction item and learning motivation scale. Data were analysed using SPSS 25.0 software. The PROCESS macro-plugin and the bootstrap method were utilised to examine the mediating and moderating effects of learning motivation and mentoring mode.
There was a positive correlation between nursing graduate students' satisfaction with mentors and the mentor-student relationship (r = 0.377, p < 0.001), learning motivation (r = 0.600, p < 0.001), and mentoring mode (r = 0.292, p 0.001). Learning motivation exerted a partial mediation effect between the mentor-student relationship and graduate students' satisfaction with mentors (mediation effect value = 0.182, 95% CI = 0.148–0.218). Mentoring mode moderated the path of learning motivation in the mentor-student relationship (interaction term coefficient = 0.031, 95% CI = 0.005–0.056).
Mentor-student relationship positively predicted nursing graduate students' satisfaction with mentors significantly. Learning motivation played a partial mediating effect between mentor-student relationship and graduate students' satisfaction with mentors and mentoring mode moderated between mentor-student relationship and learning motivation pathways. Therefore, cultivating positive teacher/helpful friend relationship, boosting students' learning motivation and improving mentoring mode techniques can all increase nursing graduate students' satisfaction with mentors.
No patient or public contribution.
To develop and validate a machine learning-based risk prediction model for delirium in older inpatients.
A prospective cohort study.
A prospective cohort study was conducted. Eighteen clinical features were prospectively collected from electronic medical records during hospitalisation to inform the model. Four machine learning algorithms were employed to develop and validate risk prediction models. The performance of all models in the training and test sets was evaluated using a combination of the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, Brier score, and other metrics before selecting the best model for SHAP interpretation.
A total of 973 older inpatient data were utilised for model construction and validation. The AUC of four machine learning models in the training and test sets ranged from 0.869 to 0.992; the accuracy ranged from 0.931 to 0.962; and the sensitivity ranged from 0.564 to 0.997. Compared to other models, the Random Forest model exhibited the best overall performance with an AUC of 0.908 (95% CI, 0.848, 0.968), an accuracy of 0.935, a sensitivity of 0.992, and a Brier score of 0.053.
The machine learning model we developed and validated for predicting delirium in older inpatients demonstrated excellent predictive performance. This model has the potential to assist healthcare professionals in early diagnosis and support informed clinical decision-making.
By identifying patients at risk of delirium early, healthcare professionals can implement preventive measures and timely interventions, potentially reducing the incidence and severity of delirium. The model's ability to support informed clinical decision-making can lead to more personalised and effective care strategies, ultimately benefiting both patients and healthcare providers.
This study was reported in accordance with the TRIPOD statement.
No patient or public contribution.
To refine fall risk assessment scale among older adults with cognitive impairment in nursing homes.
A cross-sectional survey.
Mokken analysis was conducted to refine the assessment scale based on unidimensionality, local independence, monotonicity, dimensionality, and reliability. Data were gathered from cognitively impaired older adults in a nursing home from January to February 2023. Trained nursing assistants conducted face-to-face assessments and reviewed medical records to administer the scale.
Emotion and State Dimension did not meet unidimensionality criteria (H = 0.14), particularly item Q9, which also violated local independence. Monotonicity analysis showed all items exhibited monotonic increases. After refinement at c = 0.3, the scale consists of nine items. With increasing c-values, the first seven items were ultimately retained to form the final version of the scale. Both optimised scales (9-item and 7-item) satisfied reliability requirements, with all coefficients (Cronbach's α, Guttman's lambda-2, Molenaar-Sijtsma, Latent Class Reliability Coefficient) ≥ 0.74.
The scale is suitable for assessing fall risk among older adults with cognitive impairment, with a unidimensional scale of the first seven items recommended for practical use. Future efforts should refine the scale by exploring additional risk factors, especially emotion-related ones.
The refined 7-item scale provides nursing home staff with a practical, reliable tool for assessing fall risk in cognitively impaired older adults, enabling targeted prevention strategies to enhance safety and reduce injuries.
The refined 7-item scale provides nursing home staff with a reliable, practical, and scientifically validated tool specifically designed for assessing fall risk in older adults with cognitive impairment. Its simplicity enables efficient integration into routine clinical workflows, empowering caregivers to proactively identify risk factors and implement timely, targeted interventions. This approach directly enhances resident safety by translating assessment results into actionable prevention strategies within daily care practices.
This study was reported in accordance with the STROBE guidelines.
No Patient or Public Contribution.
The health communication ability of nurses significantly impacts patients' health positively. A strong knowledge base is essential for nurses to deliver high-quality health communication.
This study aims to explore the mechanisms linking nurse health knowledge acquisition and health communication ability.
A cross-sectional study.
This cross-sectional study utilised convenience sampling of 667 nurses from nine county-level hospitals. Questionnaires were used to assess health knowledge acquisition, health literacy, health education competence and health literacy communication ability in nurses. Structural equation modelling was employed to investigate the mechanisms linking nurse health knowledge acquisition and health literacy communication ability.
The correlation analysis revealed significant positive relationships among nurses' health knowledge acquisition, health literacy, health education competence and health communication ability. The chain-mediating model indicated that health knowledge acquisition significantly influences health communication ability, with a total effect, comprising a direct effect and an indirect effect. The indirect effects were mediated either independently by health education competence or through a combination of health literacy and health education competence.
A structural equation model was developed to provide a comprehensive framework for understanding the complex interplay among nurses' health knowledge acquisition, health literacy, health education competence and health communication ability. The model demonstrates that health knowledge acquisition has a significant overall effect and indirect effect on the improvement of health communication ability. Assisting nurses in translating health knowledge into health literacy may be a crucial step in enhancing their competence in health education.
These findings enhance our understanding of the predictive effects of health knowledge acquisition on health communication ability and offer practical implications for the promoting and intervening in the health communication ability of nurses.
STROBE statement.
No patient or public contribution.
The objective of this study was to identify risk factors for enema reduction failure and to establish a combined model that integrates deep learning (DL) features and clinical features for predicting surgical intervention in intussusception in children younger than 8 months of age.
A retrospective study with a prospective validation cohort of intussusception.
The retrospective data were collected from two hospitals in south east China between January 2017 and December 2022. The prospective data were collected between January 2023 and July 2024.
A total of 415 intussusception cases in patients younger than 8 months were included in the study.
280 cases collected from Centre 1 were randomly divided into two groups at a 7:3 ratio: the training cohort (n=196) and the internal validation cohort (n=84). 85 cases collected from Centre 2 were designed as external validation cohort. Pretrained DL networks were used to extract deep transfer learning features, with least absolute shrinkage and selection operator regression selecting the non-zero coefficient features. The clinical features were screened by univariate and multivariate logistic regression analyses. We constructed a combined model that integrated the selected two types of features, along with individual clinical and DL models for comparison. Additionally, the combined model was validated in a prospective cohort (n=50) collected from Centre 1.
In the internal and external validation cohorts, the combined model (area under curve (AUC): 0.911 and 0.871, respectively) demonstrated better performance for predicting surgical intervention in intussusception in children younger than 8 months of age than the clinical model (AUC: 0.776 and 0.740, respectively) and the DL model (AUC: 0.828 and 0.793, respectively). In the prospective validation cohort, the combined model also demonstrated impressive performance with an AUC of 0.890.
The combined model, integrating DL and clinical features, demonstrated stable predictive accuracy, suggesting its potential for improving clinical therapeutic strategies for intussusception.
Sepsis is a major cause of death both globally and in the United States. Early identification and treatment of sepsis are crucial for improving patient outcomes. International guidelines recommend hospital sepsis screening programmes, which are commonly implemented in the electronic health record (EHR) as an interruptive sepsis screening alert based on systemic inflammatory response syndrome (SIRS) criteria. Despite widespread use, it is unknown whether these sepsis screening and alert tools improve the delivery of high-quality sepsis care.
The Sepsis Electronic Prompting for Timely Intervention and Care (SEPTIC) master protocol will study two distinct populations in separate trials: emergency department (ED) patients (SEPTIC-ED) and inpatients (SEPTIC-IP). The SEPTIC trials are pragmatic, multicentre, blinded, randomised controlled trials, with equal allocation to compare four SIRS-based sepsis screening alert groups: no alerts (control), nurse alerts only, prescribing clinician alerts only, or nurse and prescribing clinician alerts. Randomisation will be at the patient level. SEPTIC will be performed at eight acute-care hospitals in the greater New York City area and enrol patients at least 18 years old. The primary outcome is the percentage of patients with completion of a modified Surviving Sepsis Campaign (SSC) hour-1 bundle within 3 hours of the first SIRS alert. Secondary outcomes include time from first alert to completion of a modified SSC hour-1 bundle, time from first alert to individual bundle component order and completion, intensive care unit (ICU) transfer, hospital discharge disposition, inpatient mortality at 90 days, positive blood cultures (bacteraemia), adverse antibiotic events, sepsis diagnoses and septic shock diagnoses.
Ethics approval was obtained from the Columbia University Institutional Review Board (IRB) serving as a single IRB. Results will be disseminated in peer-reviewed journal(s), scientific meeting(s) and via social media.
ClinicalTrials.gov: NCT06117605 and
In recent years, the critical role of health literacy in diabetes management has become increasingly prominent. The aim of this study was to investigate the impact of social support on health literacy among patients with diabetes, to test the mediating role of self-efficacy and empowerment between social support and health literacy, and the moderating role of eHealth literacy.
A cross-sectional study conducted between August 2023 and June 2024.
This study adopted the cluster sampling method and conducted a questionnaire survey among 251 patients with diabetes in a tertiary hospital in Wuhu City, Anhui Province. The questionnaires included the Social Support Rating Scale, the Self-Efficacy for Diabetes scale, the Health Empowerment Scale, the eHealth Literacy Scale and the Diabetes Health Literacy Scale.
Social support was positively associated with health literacy in patients with diabetes. Self-efficacy and empowerment mediated the relationship and formed chained mediation pathways respectively. eHealth literacy has a moderating role between self-efficacy and empowerment.
The results revealed that social support influences health literacy among patients with diabetes through the mediating pathways of self-efficacy and empowerment, and that this process is moderated by eHealth literacy. These findings provide a theoretical basis and practical insights for improving health literacy among patients with diabetes.
Enhancing health literacy among people with diabetes by strengthening social support, self-efficacy and empowerment levels, while focusing on the technology-enabling role of eHealth literacy in this context.
This study adheres to the relevant EQUATOR guidelines based on the STROBE cross-sectional reporting method.
We thank all patients who participated in the study for their understanding and support.
Epidemiological evidence regarding the impact of elite athletic careers on cognitive trajectories remains contentious. Although consistent physical activity has been associated with long-term brain health, former elite athletes appear to represent a unique population. While past research has established a connection between sport-related concussions (SRCs) and later cognitive decline, less attention has been given to the cognitive function of former athletes who have not experienced SRCs. Therefore, well-structured cross-sectional studies accounting for established dementia risk factors are needed to compare mild cognitive impairment (MCI) prevalence between former elite athletes and the general population.
This cross-sectional study will be conducted at Beijing Sport University (BSU) in Beijing, China. It is designed as a comparative study, aiming to recruit a sample of around 360 participants aged 65 and above. This sample will comprise 180 former elite athletes without a history of SRCs recruited via the BSU Retirement Welfare Office (the former athlete group), and 180 age-matched individuals from the communities in three districts in Beijing (the comparison group). Participants will complete a comprehensive questionnaire covering sociodemographic information, dementia-related risk factors, current physical activity levels and, for the former athlete group specifically, details of their athletic careers. MCI and instrumental activities of daily living will be assessed using the Montreal Cognitive Assessment, Memtrax continuous recognition test and Lawton Instrumental Activities of Daily Living (IADL) scale. The primary objective is to determine whether former elite athletes without a history of SRCs have a lower MCI prevalence than the general population. The secondary objective is to assess if these former elite athletes have a reduced prevalence of amnestic MCI and impairment in IADL compared with the general population. Additionally, the study aims to explore whether specific career-related characteristics of former athletes, such as the type of sport and contact exposure, are correlated with their cognitive function and IADL abilities in later life as a secondary exploratory component. The study will calculate the crude prevalence ratios (PRs) and adjusted prevalence ratios (aPRs) with 95% CIs using the modified Poisson regression model with robust error variance.
Ethical approval was obtained from the Ethics Committee/Internal Review Board of BSU (approval number: 2024042H). All procedures will adhere to the Helsinki Declaration. The study’s findings will be provided to participants as deemed appropriate. The outcomes will be communicated through abstract presentations at national or international conferences/academic seminars, as well as through publication in a peer-reviewed journal.
ChiCTR2400085800.
Instant messaging-based applications are increasingly used to deliver interventions designed to promote health behavior change. However, the effectiveness of these interventions has not been evaluated.
This systematic review and meta-analysis aimed to evaluate the effectiveness of instant messaging-based interventions on health behavior change, addressing a gap in the literature regarding the impact of instant messaging on various health behaviors.
We conducted comprehensive searches of six electronic databases (PubMed, EMBASE, Cochrane Library, PsycINFO, CINAHL Plus, and Web of Science) from their inception until July 2024, utilizing terms related to health behavior and instant messaging. Two authors independently screened studies and extracted data. Randomized controlled trials published in English that investigated the effects of instant messaging-based interventions on health behavior change, including physical activity, sedentary behavior, sleep, diet/nutrition, cancer screening, smoking cessation, and alcohol consumption were included. We used the revised Cochrane Risk-of-Bias Tool to assess the quality of the studies.
Fifty-seven randomized controlled trials published between 2014 and 2024 were included. The results showed that compared with the control groups, instant messaging-based interventions had statistically significant differences in physical activity (SMD = 0.52, 95% CI [0.21, 0.83], p < 0.001) and sleep (SMD = −0.93, 95% CI [−1.44, −0.42], p < 0.001). It also significantly impacted smoking cessation (OR = 1.88, 95% CI [1.28, 2.7], p < 0.001). However, it did not influence sedentary behavior (SMD = 0.25, 95% CI [−0.24, 0.74], p = 0.01) or diet/nutrition (SMD = 0.01, 95% CI [−0.31, 0.34], p < 0.001).
Instant messaging-based interventions are promising in enhancing health behavior change, including physical activity, sleep, and smoking cessation. Leveraging real-time communication and multimedia content can improve patient engagement and intervention effectiveness.
The suicide rate of individuals with schizophrenia is higher than the general population. In clinical practice, it is essential to identify patients with schizophrenia who are at an elevated risk of suicide. However, previous studies may not fully account for potential factors that could influence the suicide risk among schizophrenia patients. Our study leverages machine learning to identify predictive variables from a broad range of indicators.
Cross-sectional.
A total of 131 patients with schizophrenia were recruited at the Mental Health Center of West China Hospital from August 2021 to July 2022. We collected complete blood analysis, thyroid function, inflammatory factors, childhood trauma experiences, psychological impact related to the Coronavirus Disease 2019 epidemic, sleep quality, psychological distress, income level and other demographic data. We utilised machine learning algorithms to predict the suicide risk of patients with the above features. The Shapley values were used to illustrate important predictive variables of suicide risk.
We gathered important variables for predicting suicide risk of patients with schizophrenia, such as the Nurses' Observation Scale for Inpatient Evaluation factor, neutrophil count, psychological impact during Coronavirus Disease 2019 epidemic, prolactin level and plasma thromboplastin component level.
The features identified in this study are anticipated to aid in the clinical identification of suicide risk in individuals with schizophrenia in the future. This study also promoted improvements in the suicide prediction model among patients with schizophrenia.
This study identified key predictive variables for suicide risk in schizophrenia patients using machine learning. Our findings will enhance clinical tools for assessing suicide risk in schizophrenia, potentially leading to more effective prevention strategies. This advancement holds promise for improving suicide prevention efforts and tailoring interventions to individuals' specific risk profiles.
STROBE Statement (for cross-sectional studies).
None.
This study investigates how observed workplace ostracism affects nurses' helping behaviour from a bystander's perspective, examining the mediating roles of moral courage and employee resilience to inform strategies for fostering workplace harmony in nursing settings.
A cross-sectional study design was adopted.
A survey of 346 nurses from two Grade III, Level A hospitals in Henan, China, utilised scales measuring workplace ostracism, moral courage, helping behaviour and employee resilience. SPSS Statistics 26.0, Mplus 8.3 and the SPSS macro program Process 4.1 plugin were used to test the associations among variables.
Observed workplace ostracism positively correlated with nurses' helping behaviour, with moral courage partially mediating this relationship. Employee resilience moderated both the link between observed workplace ostracism and moral courage, and the indirect effect of observed workplace ostracism on helping behaviour through moral courage.
Nurses with high levels of resilience demonstrate moral courage when observing workplace ostracism and engage in helping behaviours towards those ostracised.
This study examines how workplace ostracism undermines nursing team cohesion and individual well-being. It highlights that bolstering nurses' resilience and moral courage can alleviate these adverse effects, thereby improving patient care quality. Nursing managers are advised to adopt targeted strategies, such as resilience training, to mitigate workplace ostracism.
This study employs a questionnaire to explore nurses' views of workplace ostracism and helping behaviours, aiming to inform strategies for fostering nursing team harmony and improving care quality.
This study strictly follows the STROBE reporting guidelines to ensure the clarity and credibility of the research findings.
Data were collected from hospital nurses through electronic questionnaires.
by Lijun Jiang, Qian Yu, Hui Li, Fudong Wang, Feng Liu, Zhenxing Xu
ObjectiveTo determine the association between blood pressure variability (BPV), coagulation indexes, and germinal matrix-intraventricular hemorrhage (GMH-IVH) in preterm infants with gestational age ≤ 32 weeks. In addition, we aimed to determine whether the combination can predict the occurrence and outcome of GMH-IVH.
MethodsThis retrospective study included 106 preterm infants. According to the presence of GMH-IVH, the preterm infants were divided into GMH-IVH (51 patients) and no GMH-IVH (55 patients) groups. Furthermore, according to the short-term prognoses, the GMH-IVH group was subdivided into good outcome (30 patients) and poor outcome (21 patients) groups. Coagulation function and BPV indexes were collected at admission. Univariate analysis, logistic regression model, and receiver operating characteristic curve were used to analyze the relationship between indexes and the occurrence and outcome of GMH-IVH in preterm infants.
ResultsUnivariate analysis showed that the difference between maximum and minimum (Max-Min); standard deviation (SD); coefficient of variation (CV) of BPV, prothrombin time (PT), international normalized ratio (INR), activated partial thromboplastin time (APTT), and proportion of premature rupture of membranes (PROM) were higher in the GMH-IVH group than the no GMH-IVH group (P ). Logistic regression analysis showed that INR and DBP SD were directly correlated with GMH-IVH, and the joint curve had the largest area under the curve (AUC) (82.4% sensitivity and 79.7% specificity). BPV SD, BPV CV, APTT, and INR were higher in the poor outcome group than in the good outcome group (P ). Logistic regression analysis showed that INR and DBP SD were directly correlated with poor outcomes in preterm infants with GMH-IVH. The joint curve had the largest AUC (sensitivity 76.2% and specificity 90.0%).
ConclusionIncreased INR and DBP SD are directly associated factors for the developement and poor short-term outcome of GMH-IVH, and combined monitoring of INR and DBP SD has certain reference value for the early identification and prognosis evaluation of GMH-IVH in preterm infants with gestational age ≤ 32 weeks.
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