Guiyuan Ma

Background: Patients with cancer often experience substantial fluctuations in psychological states during disease management. Traditional research tools are limited in capturing these dynamic changes in real time, constraining clinicians’ understanding of patients’ true conditions. Ecological momentary assessment (EMA) enables high-frequency, real-time data collection, providing patient-reported data with greater ecological validity. However, the effectiveness of EMA studies critically depends on patient compliance, and reported compliance rates vary widely, with a lack of systematic quantitative synthesis. Objective: This study aims to systematically review and quantitatively analyze compliance with EMA among patients with cancer, and to examine whether EMA design characteristics were associated with compliance. Methods: Web of Science, PubMed, Embase, Cochrane Library, CINAHL, PsycINFO, CNKI, and Wanfang databases were searched for literature published up to April 30, 2026. Compliance was defined as completed prompts divided by delivered prompts. Single-group proportions were pooled using logit transformation and random-effects models with the Hartung-Knapp-Sidik-Jonkman adjustment. Prediction intervals were calculated to describe the expected distribution of compliance in future comparable settings. Subgroup analyses, univariable meta-regressions, leave-one-out sensitivity analyses, and tests for small-study effects were performed. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Studies Reporting Prevalence Data, methodological reporting quality was assessed using a modified Checklist for Reporting EMA Studies, and certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach. Results: Twenty-three studies involving 13,565 participants were included. The pooled compliance rate was 78.55% (95% CI 73.48%‐82.87%), with a prediction interval of 48.59%‐93.41%. Subgroup analyses identified no robust differences across study characteristics. Although study length showed a statistically significant subgroup test, the result was not stable after excluding singleton categories. Meta-regression analyses similarly found no significant linear associations for study length, prompts per day, items per prompt, or assessment window. Leave-one-out analyses showed that no single study drove the pooled estimate. Regarding the risk of bias, 2 studies were judged as low, while 21 were judged as moderate risk. Quality scores ranged from 6.5 to 9.0, and the certainty of evidence for the pooled compliance rate was rated as very low according to the Grading of Recommendations Assessment, Development, and Evaluation approach. Conclusions: Overall compliance with EMA among patients with cancer was moderate to high, suggesting that repeated real-world assessment may be feasible in oncology research settings. Nevertheless, the very high heterogeneity, wide prediction interval, and very low certainty of evidence indicate that compliance is context-dependent. The pooled estimate should therefore be interpreted as an approximate benchmark rather than a universal expected rate. Future oncology EMA studies should use standardized compliance denominators, report missing prompts transparently, and prospectively evaluate patient-centered design strategies that reduce burden while preserving data quality.

Introduction: Perinatal mental health problems are prevalent during pregnancy and the postpartum period and can have substantial consequences for both mothers and infants. Chatbot-based interventions represent a promising and scalable approach to perinatal mental health support. However, the evidence base remains heterogeneous and has not been systematically mapped. This scoping review aims to synthesise existing research on chatbot-based interventions for perinatal mental health, focusing on study characteristics, intervention features, outcomes and implementation challenges. Methods and analysis: This scoping review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols guidelines. A systematic search will be conducted from database inception to May 2026 to identify quantitative, qualitative and mixed-methods studies focusing on chatbot-based interventions for perinatal mental health. Two reviewers will independently screen studies and extract data, with discrepancies resolved through discussion or consultation with a third reviewer. Data will be synthesised descriptively. Ethics and dissemination: This study is based on publicly available literature and does not require ethical approval. The findings will be disseminated through peer-reviewed journal publications and presented at academic conferences related to perinatal and child health and digital health.

Objectives: To identify the latent profiles of health behaviour abilities among patients with haematological malignancies and to explore the underlying population heterogeneity and influencing factors. Design: A cross-sectional study conducted between October and December 2024. Setting: Data were collected from three provincial hospitals in China. Participants: A total of 486 hospitalised patients were recruited. Outcome measures: Data were collected using a demographic questionnaire, the Sense of Coherence Scale-13, the Perceived Social Support Scale and the Self-rated Abilities for Health Practices Scale. Latent profile analysis was performed to identify distinct classifications of health behaviour abilities and multivariate logistic regression was employed to determine the influencing factors. Results: The mean score of health behaviour abilities among the participants was 47.96 (SD 23.29). Three profiles were identified, showcasing substantial population heterogeneity: ‘exercise-weak survival profile’ (41%), ‘exercise-deficient developmental profile’ (45%) and ‘balanced-robust profile’ (14%). Multivariable regression revealed that age, education level, social support, financial toxicity and psychological distress were influencing factors determining profile membership. Conclusions: Health behaviour abilities in patients with haematological malignancies were at a low level with three distinct latent profiles. Clinical healthcare providers should implement targeted interventions based on the characteristics of patients’ health behaviour abilities to enhance their self-management capabilities.

Evaluating Master of Nursing Specialist (MNS) students' knowledge, attitudes, and practice regarding catheter‐related thrombosis (CRT) prevention and treatment is beneficial in developing effective CRT training for them. This study thus aimed to explore their knowledge, attitudes, and practice toward CRT. This was a cross‐sectional study that included 1046 MNS students in China. The results revealed that the median scores for the participants' knowledge, attitudes, and practice were 12 (interquartile range: 9–14), 55 (interquartile range: 50–65), and 62 (interquartile range: 51–71), respectively. There was a positive correlation between knowledge and attitudes, as well as attitudes and practice. Age, school level, proactively learning CRT knowledge, gender, enrolment method, participation in clinical practice time, and attendance at CRT training were related to knowledge (p < 0.05). Attendance at CRT training and experience using thrombosis risk assessment forms were related to attitude and practice (p < 0.05). Gender and experience caring for CRT patients were associated with practice (p < 0.05). MNS students have insufficient knowledge of CRT prevention and treatment, and the practice needs to be improved. However, their attitudes are positive.

Background: Cancer indeed represents a significant public health challenge, and unplanned extubation of peripherally inserted central catheter (PICC-UE) is a critical concern in patient safety. Identifying independent risk factors and implementing high-quality assessment tools for early detection in high-risk populations can play a crucial role in reducing the incidence of PICC-UE among patients with cancer. Precise prevention and treatment strategies are essential to improve patient outcomes and safety in clinical settings. Objective: This study aims to identify the independent risk factors associated with PICC-UE in patients with cancer and to construct a predictive model tailored to this group, offering a theoretical framework for anticipating and preventing PICC-UE in these patients. Methods: Prospective data were gathered from January to December 2022, encompassing patients with cancer with PICC at Xiangya Hospital, Central South University. Each patient underwent continuous monitoring until the catheter’s removal. The patients were categorized into 2 groups: the UE group (n=3107) and the non-UE group (n=284). Independent risk factors were identified through univariate analysis, the least absolute shrinkage and selection operator (LASSO) algorithm, and multivariate analysis. Subsequently, the 3391 patients were classified into a train set and a test set in a 7:3 ratio. Utilizing the identified predictors, 3 predictive models were constructed using the logistic regression, support vector machine, and random forest algorithms. The ultimate model was selected based on the receiver operating characteristic (ROC) curve and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) synthesis analysis. To further validate the model, we gathered prospective data from 600 patients with cancer at the Affiliated Hospital of Qinghai University and Hainan Provincial People’s Hospital from June to December 2022. We assessed the model’s performance using the area under the curve of the ROC to evaluate differentiation, the calibration curve for calibration capability, and decision curve analysis (DCA) to gauge the model’s clinical applicability. Results: Independent risk factors for PICC-UE in patients with cancer were identified, including impaired physical mobility (odds ratio [OR] 2.775, 95% CI 1.951-3.946), diabetes (OR 1.754, 95% CI 1.134-2.712), surgical history (OR 1.734, 95% CI 1.313-2.290), elevated D-dimer concentration (OR 2.376, 95% CI 1.778-3.176), targeted therapy (OR 1.441, 95% CI 1.104-1.881), surgical treatment (OR 1.543, 95% CI 1.152-2.066), and more than 1 catheter puncture (OR 1.715, 95% CI 1.121-2.624). Protective factors were normal BMI (OR 0.449, 95% CI 0.342-0.590), polyurethane catheter material (OR 0.305, 95% CI 0.228-0.408), and valved catheter (OR 0.639, 95% CI 0.480-0.851). The TOPSIS synthesis analysis results showed that in the train set, the composite index (Ci) values were 0.00 for the logistic model, 0.82 for the support vector machine model, and 0.85 for the random forest model. In the test set, the Ci values were 0.00 for the logistic model, 1.00 for the support vector machine model, and 0.81 for the random forest model. The optimal model, constructed based on the support vector machine, was obtained and validated externally. The ROC curve, calibration curve, and DCA curve demonstrated that the model exhibited excellent accuracy, stability, generalizability, and clinical applicability. Conclusions: In summary, this study identified 10 independent risk factors for PICC-UE in patients with cancer. The predictive model developed using the support vector machine algorithm demonstrated excellent clinical applicability and was validated externally, providing valuable support for the early prediction of PICC-UE in patients with cancer.

Background: Developing Internet+home care (IHC) services is a promising way to address the problems related to population aging, which is an important global issue. However, IHC services are in their infancy in China. Limited studies have investigated the willingness and demand of nurses in municipal hospitals to provide IHC services. Objective: This study aims to investigate the willingness and demand of nurses in municipal hospitals in China to provide IHC services and analyze the factors to promote IHC development in China. Methods: This cross-sectional study used multistage sampling to recruit 9405 nurses from 10 hospitals in 5 regions of China. A self-designed questionnaire with good reliability and validity was used to measure nurses’ willingness and demand for providing IHC services. Data analysis used the chi-square test, Welch t test, binary logistic regression analysis, and multiple linear regression analysis. Results: Nurses were highly willing to provide IHC services and preferred service distances of 60% was the expected service pay sharing. Job title, educational level, monthly income, and marital status were associated with nurses’ willingness to provide IHC services in binary logistic regression analysis. Supervising nurses were 1.177 times more likely to express a willingness to provide IHC services than senior nurses. Nurses with a bachelor's degree had a 1.167 times higher likelihood of expressing willingness to provide IHC services than those with a junior college education or lower. Married nurses were 1.075 times more likely to express a willingness than unmarried nurses. A monthly income >¥10,000 increased the likelihood of nurses’ willingness to provide IHC services, by 1.187 times, compared with an income <¥5000. Nurses’ total mean demand score for IHC services was 17.38 (SD 3.67), with the highest demand being privacy protection. Multiple linear regression analysis showed that job title, monthly income, and educational level were associated with nurses’ demand for IHC services. Supervising nurses (B=1.058, P<.001) and co-chief nurses or those with higher positions (B=2.574, P<.001) reported higher demand scores than senior nurses. Monthly incomes of ¥5000 to ¥10,000 (B=0.894, P<.001) and >¥10,000 (B=1.335, P<.001), as well as a bachelor's degree (B=0.484, P=.002) and at least a master's degree (B=1.224, P=.02), were associated with higher demand scores compared with a monthly income <¥5000 and junior college education or lower, respectively. Conclusions: Nurses in municipal hospitals showed a high willingness and demand to provide IHC services, with differences in willingness and demand by demographic characteristics. Accordingly, government and hospitals should regulate the service period, service distance, and other characteristics according to nurses’ willingness and demand and establish relevant laws and regulations to ensure the steady and orderly development of IHC services.

BACKGROUND Cancer indeed represents a significant public health challenge, and unplanned extubation of peripherally inserted central catheter (PICC-UE) is a critical concern in patient safety. Identifying independent risk factors and implementing high-quality assessment tools for early detection in high-risk populations can play a crucial role in reducing the incidence of PICC-UE among patients with cancer. Precise prevention and treatment strategies are essential to improve patient outcomes and safety in clinical settings. OBJECTIVE This study aims to identify the independent risk factors associated with PICC-UE in patients with cancer and to construct a predictive model tailored to this group, offering a theoretical framework for anticipating and preventing PICC-UE in these patients. METHODS Prospective data were gathered from January to December 2022, encompassing patients with cancer with PICC at Xiangya Hospital, Central South University. Each patient underwent continuous monitoring until the catheter’s removal. The patients were categorized into 2 groups: the UE group (n=3107) and the non-UE group (n=284). Independent risk factors were identified through univariate analysis, the least absolute shrinkage and selection operator (LASSO) algorithm, and multivariate analysis. Subsequently, the 3391 patients were classified into a train set and a test set in a 7:3 ratio. Utilizing the identified predictors, 3 predictive models were constructed using the logistic regression, support vector machine, and random forest algorithms. The ultimate model was selected based on the receiver operating characteristic (ROC) curve and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) synthesis analysis. To further validate the model, we gathered prospective data from 600 patients with cancer at the Affiliated Hospital of Qinghai University and Hainan Provincial People’s Hospital from June to December 2022. We assessed the model’s performance using the area under the curve of the ROC to evaluate differentiation, the calibration curve for calibration capability, and decision curve analysis (DCA) to gauge the model’s clinical applicability. RESULTS Independent risk factors for PICC-UE in patients with cancer were identified, including impaired physical mobility (odds ratio [OR] 2.775, 95% CI 1.951-3.946), diabetes (OR 1.754, 95% CI 1.134-2.712), surgical history (OR 1.734, 95% CI 1.313-2.290), elevated D-dimer concentration (OR 2.376, 95% CI 1.778-3.176), targeted therapy (OR 1.441, 95% CI 1.104-1.881), surgical treatment (OR 1.543, 95% CI 1.152-2.066), and more than 1 catheter puncture (OR 1.715, 95% CI 1.121-2.624). Protective factors were normal BMI (OR 0.449, 95% CI 0.342-0.590), polyurethane catheter material (OR 0.305, 95% CI 0.228-0.408), and valved catheter (OR 0.639, 95% CI 0.480-0.851). The TOPSIS synthesis analysis results showed that in the train set, the composite index (Ci) values were 0.00 for the logistic model, 0.82 for the support vector machine model, and 0.85 for the random forest model. In the test set, the Ci values were 0.00 for the logistic model, 1.00 for the support vector machine model, and 0.81 for the random forest model. The optimal model, constructed based on the support vector machine, was obtained and validated externally. The ROC curve, calibration curve, and DCA curve demonstrated that the model exhibited excellent accuracy, stability, generalizability, and clinical applicability. CONCLUSIONS In summary, this study identified 10 independent risk factors for PICC-UE in patients with cancer. The predictive model developed using the support vector machine algorithm demonstrated excellent clinical applicability and was validated externally, providing valuable support for the early prediction of PICC-UE in patients with cancer.