Conectar
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.
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)
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.
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.
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.
To investigate the physical activity levels of lung cancer survivors, analyse the influencing factors, and construct a predictive model for the physical activity levels of lung cancer survivors based on machine learning algorithms.
This was a cross-sectional study.
Convenience sampling was used to survey lung cancer survivors across 14 hospitals in eastern, central, and western China. Data on demographic, disease-related, health-related, physical, and psychosocial factors were also collected. Descriptive analyses were performed using SPSS 25.0, and predictors were identified through multiple logistic regression analyses. Four machine learning models—random forest, gradient boosting tree, support vector machine, and logistic regression—were developed and evaluated based on the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC), accuracy, precision, recall, and F1 score. The best model was used to create an online computational tool using Python 3.11 and Flask 3.0.3. This study was conducted and reported in accordance with the TRIPOD guidelines and checklist.
Among the 2231 participants, 670 (30%), 1185 (53.1%), and 376 (16.9%) exhibited low, moderate, and high physical activity levels, respectively. Multivariate logistic regression identified 15 independent influencing factors: residential location, geographical region, religious beliefs, histological type, treatment modality, regional lymph node stage, grip strength, 6-min walking distance, globulin, white blood cells, aspartate aminotransferase, blood urea, MDASI score, depression score, and SRAHP score. The random forest model performed best among the four algorithms, achieving AUC-ROC values of 0.86, 0.70, 0.72, and 0.67, respectively, and was used to develop an online predictive tool (URL: http://10.60.32.178:5000).
This study developed a machine learning model to predict physical activity levels in lung cancer survivors, with the random forest model demonstrating the highest accuracy and clinical utility. This tool enables the early identification of low-activity survivors, facilitating timely, personalised rehabilitation and health management.
The development of a predictive model for physical activity levels in lung cancer survivors can help clinical medical staff identify survivors with relatively low physical activity levels as early as possible. Thus, personalised rehabilitation plans can be formulated to optimise quality of life during their survival period.
Physical activity has been used as a nonpharmacological intervention in cancer patient rehabilitation plans. However, a review of past studies has shown that lung cancer survivors generally have low physical activity levels. In this study, we identified the key factors influencing physical activity among lung cancer survivors through a literature review. We constructed a prediction model for their physical activity levels using machine learning algorithms. Clinical medical staff can use this model to identify patients with low physical activity levels early and to develop personalised intervention plans to improve their quality of life during survival.
The study adhered to the relevant EQUATOR reporting guidelines, the TRIPOD Checklist for Prediction Model Development and Validation.
During the data collection phase, participants were recruited to complete the questionnaires.
Individuals with systemic lupus erythematosus (SLE) often suffer from sleep disturbance, which exhibits heterogeneity. Whether it could be grouped into different clusters remains unknown, posing challenges to the development of personalised interventions to address sleep disturbance.
To examine clusters of sleep disturbance and associated factors in people with SLE.
Cross-sectional design.
From November 2023 to January 2024, people diagnosed with SLE were recruited by a convenience sampling approach. Data were collected via an online platform Wenjuanxing. Sleep disturbance was evaluated by the Pittsburgh Sleep Quality Index (PSQI). Other information, such as disease activity, pain, fatigue, depression and anxiety was also collected using validated instruments. Latent profile analysis was performed to reveal the distinct clusters of sleep disturbance. Multiple logistic regression analysis was performed to investigate factors associated with the clusters.
A total of 538 participants were included, with a response rate of 85.1% (538/632). Only those with sleep disturbance (PSQI > 5) were included in the final analyses. Participant mean age was 32.9 (SD = 8.4) years and 402 (92.6%) were females. All had sleep disturbance (PSQI > 5) and their mean PSQI was 8.8 (SD = 2.9). Three distinct clusters were identified: mild sleep disturbance with poor sleep quality, adequate sleep duration and good daytime functioning (50.7%), mild sleep disturbance with poor sleep quality, adequate sleep duration and poor daytime functioning (30.9%) and moderate sleep disturbance with poor sleep quality, inadequate sleep duration and impaired daytime functioning (18.4%). There are both overlaps and unique aspects in terms of factors associated with each cluster of sleep disturbance, including age, body mass index, cardiovascular system damage, musculoskeletal system damage, depression and anxiety.
Sleep disturbance in patients with SLE showed three distinct clusters, with each cluster having slightly different predisposing factors.
In clinical practice, nurses are recommended to prioritise assessment and interventions for those at-risk subgroups. They could also use the above information to develop and provide personalised interventions to address the unique needs of each cluster of sleep disturbance.
Checklist for reporting of survey studies.
No patient or public contribution.
Lower extremity lymphedema (LEL) is a debilitating complication for patients with gynecologic cancer. A series of strategies have been recommended to mitigate the risk of LEL and improve patient outcomes; however, investigation into LEL risk management behaviours in this population is limited, and the absence of reliable and valid tools is an important reason.
To develop and evaluate the psychometric properties of the lower extremity lymphedema risk management behaviours questionnaire (LELRMBQ) for Chinese patients with gynaecologic cancer.
This was a methodological study.
Initial items were generated using a literature review. The initial LELRMBQ was refined, and its content validity was evaluated by conducting two rounds of expert consultation and a pilot study. Psychometric testing of 389 participants recruited by convenience sampling was conducted from December 2022 to June 2023. Exploratory factor analysis (EFA; subsample 1, N = 158) and confirmatory factor analysis (CFA; subsample 2, N = 231) were performed separately to determine the multi-dimensional structure of the questionnaire. Known-group validity, internal consistency reliability, and test–retest reliability were also evaluated.
A total of 25 items with satisfactory content validity were included in psychometric testing. The EFA identified a four-factor structure, comprising 18 items, which explained 74.49% of the total variance. The CFA supported this structure with acceptable fit indices. Known-group validity was partially supported by significant differences in total LELRMBQ scores among groups with different education levels, residence, cancer type, and LEL awareness. Internal consistency and temporal stability were acceptable.
The 18-item LELRMBQ demonstrated sufficient reliability and validity as a tool for measuring LEL risk management behaviours in patients with gynaecologic cancer.
The LELRMBQ has potential applicability in assessing LEL risk management behaviours, identifying gaps in educational practices, tailoring effective interventions, and evaluating intervention effectiveness.
This manuscript followed the STROBE guidelines.
Patients with gynecologic cancer participated in this study and provided the data through the survey.
To explore the association between psychological capital and psychological distress in stroke patient–spouse dyads and examine the mediating effect of relationship satisfaction in this association.
A population of 207 stroke patient-spouse dyads completed the Positive Psychological Capital Questionnaire, Quality of Relationship Index, and Kessler Psychological Distress Scale. A dyadic analysis was conducted using the actor-partner interdependence mediation model.
In stroke-affected couples, a noteworthy interaction exists between moderately elevated levels of psychological capital (p < 0.01). Patients exhibit significantly diminished psychological capital and heightened psychological distress compared to their spouses (t = −5.429, p < 0.001; t = 2.536, p < 0.05). Conversely, there is no significant variance in relationship satisfaction between patients and the partners (t = −0.920, p > 0.05). Patient relationship satisfaction acts as a mediator in the correlation between dyadic psychological capital and patient psychological distress (β = −0.020, p < 0.05; β = −0.011, p < 0.05). Similarly, spousal relationship satisfaction serves as a mediator in the connection between dyadic psychological capital and spousal psychological distress (β = −0.011, p < 0.05; β = −0.020, p < 0.05).
Psychological distress was reduced when psychological capital or relationship satisfaction in stroke dyads was promoted, and relationship satisfaction is an important mediator of the impact of psychological capital on psychological distress in the dyads. Healthcare providers should pay equal attention to spouses and implement dyadic psychological capital interventions centered on stroke couples to enhance relationship satisfaction and reduce psychological distress.
Although cancer is a worldwide public health problem, it can be detected early and prevented through cancer screening. However, not all individuals are motivated to undergo cancer screening. Current studies have revealed that decision aids can impact decision-related outcomes among individuals at risk of cancer. However, their efficacy on decision knowledge and decision conflict remains unclear.
The purpose of this meta-analysis was to appraise the efficacy of decision aids on decision knowledge and conflict among people at risk of cancer.
Nine electronic databases were utilized to search the literature until October 31, 2024. The Cochrane Risk of Bias Tool 2.0 and the Grading of Recommendations Assessment, Development, and Evaluation approach were used to evaluate the certainty of evidence. The data were analyzed using Stata 16.0.
Thirteen relevant studies with 2971 participants published between 2002 and 2023. The pooled results showed that decision aids significantly improved decision knowledge (SMD = 0.45, 95% CI [0.19–0.72], p = 0.00) and decreased decision conflict (SMD = −0.47, 95% CI [−0.73 to −0.21], p = 0.00). Subgroup analyses revealed that the framework, format, population, and duration of decision aids can influence their effects on decision knowledge and decision conflict among people at risk of cancer.
This meta-analysis illuminates that decision aids are effective for improving decision knowledge and diminishing decision conflict among people at risk of cancer. The framework, format, population, and duration should be considered when developing decision aids. Our findings may suggest future directions for assisting people at risk of cancer in making informed decisions about cancer screening. Additional trustworthy research is required to verify these findings.
To explore the heterogeneity of disease-specific anxiety profiles among patients with chronic obstructive pulmonary disease (COPD) using latent profile analysis (LPA), and to identify the associations between distinct anxiety subtypes and inhaler medication adherence in patients with COPD.
Adherence to inhaled medication among patients with COPD continues to be suboptimal. Anxiety, a common comorbidity, may exacerbate this issue. However, the specific relationship between anxiety and adherence to inhaled medications remains unclear.
A prospective cohort study was conducted following the STROBE Checklist.
A prospective observational study employed the Anxiety Inventory for Respiratory Disease (AIR) to assess disease-specific anxiety in patients with COPD. Inhaler medication adherence was evaluated using the Test of Adherence to Inhalers (TAI) 6 months after initiating treatment. Latent Profile Analysis (LPA) was performed to identify distinct anxiety subtypes. Multiple linear regression analysis was conducted to examine the associations between identified anxiety subtypes and adherence dimensions, adjusting for sociodemographic and clinical variables.
Among 298 COPD patients, the overall AIR score was 5 (IQR: 2–11). Using LPA, three distinct anxiety subtypes were identified: Low Anxiety—Irritable Subtype (57.05%), Moderate Anxiety—Tense Subtype (26.85%) and High Anxiety—Anticipatory Subtype (16.10%). Through multiple linear regression analysis, the High Anxiety—Anticipatory Subtype was significantly associated with lower inhaler medication adherence among COPD patients.
This study revealed three latent profiles of disease-specific anxiety among COPD patients. The High Anxiety–Anticipatory Subtype was associated with a lower inhaler medication adherence in individuals with COPD after initiating treatment.
Identifying the relationship between disease-specific anxiety and inhaler medication adherence in patients with COPD after initiating treatment underscores the need for healthcare providers to assess anxiety during patient visits and prioritise patients with high anticipatory anxiety. When high anxiety adversely affects inhaler medication adherence, targeted interventions should be developed to improve adherence and prognosis.
No patient or public contribution.
Deep vein thrombosis (DVT) is a frequent complication following endovascular thrombectomy (EVT) in patients with acute ischaemic stroke (AIS), potentially leading to fatal pulmonary embolism (PE). Identifying patients early at high risk for DVT is clinically important. This study developed and validated a nomogram combining laboratory findings and clinical characteristics to predict the risk of lower-extremity DVT after EVT in patients with AIS.
This retrospective multicentre observational study was conducted in two tertiary hospitals in China, enrolling 640 patients who underwent ultrasonography for DVT diagnosis within 10 days following EVT. Data on medical history, examination and laboratory results were collected for logistic regression analyses to develop a DVT risk nomogram.
Logistic regression analyses identified critical predictors of DVT: lower limb National Institutes of Health Stroke Scale (NIHSS) score ≥ 2, elevated D-dimer levels (≥ 1.62 mg/L) and prolonged puncture-to-recanalization time (PRT ≥ 66 min). The nomogram demonstrated good discriminative ability (AUC 0.741–0.822) and clinical utility across internal and external validation cohorts. Additionally, the presence of DVT was significantly associated with reduced functional independence at 90 days post-EVT, highlighting the negative impact of DVT on patient recovery (OR = 3.85; 95% CI: 2.18–6.78; p < 0.001).
The study provides a practical clinical tool for early detection and intervention in patients with AIS at high risk for DVT following EVT. Early identification and intervention may help improve outcomes in patients with AIS undergoing EVT.
This nomogram helps in the early detection and proactive management of DVT in AIS patients, which can reduce severe complications and improve patient recovery outcomes.
No patient or public contributions were involved in this study due to its retrospective design, where data were utilised from existing medical records without direct patient interaction.
To evaluate the effectiveness of nurse-led care (NLC) in patients with rheumatoid arthritis on disease activity, physical function, fatigue, satisfaction, pain, and quality of life.
Rheumatoid arthritis is a chronic autoimmune disease, which may not respond to insufficient rheumatology care capacity and workforce shortage. NLC is a care delivery model that can help address this shortage and improve disease management.
Systematic review and meta-analysis.
Nine databases were independently searched by two reviewers for eligible studies. Randomised controlled studies evaluating the effects of NLC on disease activity, physical function, fatigue, satisfaction, and other outcomes were included. The cochrane risk of bias tool was used to assess the risk of bias.
A total of nine studies involving 1447 participants were included. The pooled results indicated that no significant difference in disease activity was found at 0.5 years of follow-up (SMD: −0.33, 95% CI [−0.70, 0.04]), and a significant difference was seen in favour of NLC at 1 year (SMD: −0.35, 95% CI [−0.48, −0.10]), and 2 years (SMD: −0.29, 95% CI [−0.48, −0.10]). Moreover, no significant difference was found in fatigue and satisfaction at 0.5 years of follow-up, whereas differences in favour of NLC were seen at 1 year. In addition, no significant difference was found in physical function, pain, and quality of life.
This review indicated that NLC was not inferior to other types of care, and even had a better positive impact on disease activity, fatigue, and satisfaction for patients with rheumatoid arthritis.
Our study demonstrates that NLC is an effective approach to managing rheumatoid arthritis and recommends medical practitioners be well-versed in its importance.
Patients or public members were not directly involved in this study.
ClinicalTrials.gov identifier: CRD42022355963
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