Objective:This study aimed to determine the temporal trends in sleep duration among Chinese adults. Methods:In this series of repeated nationally representative cross-sectional surveys (China Chronic Disease and Risk Factors Surveillance) conducted between 2010 and 2018, a total of 645,420 adult participants (97,741 in 2010; 175,749 in 2013; 187,777 in 2015; and 184,153 in 2018) were included in the trend analysis. Linear and logistic regression models were utilized to assess trends in sleep duration. Results:In 2018, the estimated overall mean sleep duration among the Chinese adult population was 7.58 (SD, 1.45) hours per day, with no significant trend from 2010. A significant increase in short sleep duration (≤ 6 hours) was observed in the total population, from 15.3% (95% CI: 14.1%-16.5%) in 2010 to 18.5% (95% CI: 17.7%-19.3%) in 2018 ( P < 0.001). Similarly, the trend in long sleep duration (> 9 hours) was also significant, increasing in weighted prevalence from 7.2% (95% CI: 6.3%-8.1%) in 2010 to 9.0% (95% CI: 8.2%-9.9%) in 2018 ( P < 0.001). Conclusion:The prevalence of both short and long sleep durations significantly increased among Chinese adults from 2010 to 2018, highlighting the urgency of health initiatives to promote optimal sleep duration in China.

Objective:To identify factors associated with smoking relapse or non-attempt within one year in COPD patients and to develop a predictive model for early identification of high-risk individuals to guide targeted interventions. Methods:Based on the health ecology model, a questionnaire integrating factors affecting smoking cessation was developed. We enrolled 221 COPD patients from a tertiary hospital in Tianjin and categorized them into smoking cessation success or failure groups. Mann-Whitney U-tests, χ2 -tests, and logistic regression were used to identify predictors. A nomogram prediction model was developed using significant factors. Model performance was evaluated via calibration plot, Hosmer-Lemeshow test, concordance index (C-index), decision curve analysis (DCA), and clinical impact curve (CIC). Results:Among 221 patients, 92 successfully quit smoking and 129 failed. Multivariate analysis identified age (OR = 0.922, P < 0.001), GOLD grade (OR = 0.257, P < 0.001), and death anxiety score (OR = 0.930, P = 0.001) as protective factors against cessation failure, while depression score (OR = 1.107, P < 0.001) and quit-smoking partner complaints score (OR = 1.075, P < 0.001) were risk factors. The prediction model demonstrated good discrimination (C-index = 0.876) and calibration (Hosmer-Lemeshow test P = 0.350). DCA and CIC confirmed the model's clinical utility. Conclusion:Younger age, mild/moderate GOLD grade, higher depression score, lower death anxiety, and higher partner complaints increase the risk of smoking cessation failure in COPD patients. The developed model facilitates early identification of high-risk patients for targeted intervention to improve quit rates.

Background: Chronic obstructive pulmonary disease (COPD) imposes a substantial public health burden worldwide. Current COPD self-care management is largely nurse-led and often limited in accessibility and scalability. Artificial intelligence (AI) offers new opportunities to deliver personalized and scalable self-care support, yet evidence of its effectiveness remains limited. This randomized controlled trial (RCT) aims to evaluate an intelligent self-care platform designed to improve self-care behaviors in patients with COPD. Methods: A total of 104 patients diagnosed with COPD will be recruited and randomly assigned (1:1) to either the intervention group (n=52) or the control group (n=52). Participants in the intervention group will receive access to a 24/7 AI-enabled intelligent self-care platform for 3 months, while those in the control group will receive standard care. Participants will be encouraged to use the platform daily, and system logs will automatically record usage to monitor adherence. The primary outcomes will include the Chinese version of the Self-Care in COPD Inventory (SC-COPDI-C) and the Chinese version of the Self-Care Self-Efficacy Scale (SCSES-C). Data will be collected at baseline, 1 month, and 3 months. Outcome assessors will remain blinded to group allocation. Safety monitoring will include surveillance of adverse events and periodic review of AI-generated responses to identify potential medical inaccuracies. Generalized estimating equations (GEE) will be used to evaluate improvements in self-care behaviors. Discussion: This trial will evaluate the effectiveness of an AI-enabled intelligent self-care platform designed to enhance self-care behaviors and self-efficacy in patients with COPD. By integrating large language models (LLMs), retrieval-augmented generation (RAG), and a structured clinical knowledge base, the platform aims to provide personalized and evidence-based support for COPD self-management. The findings will provide important evidence regarding the feasibility, safety, and clinical impact of AI-driven self-care interventions and may inform the development of scalable digital health strategies for chronic disease management. Trial Registration: The study protocol was registered on Chinese Clinical Trial Registry (ChiCTR2600123383).

Background: Self-care is critical for improving outcomes and quality of life in patients with chronic obstructive pulmonary disease (COPD), yet substantial barriers remain. The Middle-Range Theory of Self-Care of Chronic Illness provides a framework for systematically examining these obstacles. Aim: To explore barriers to self-care among patients with COPD. Methods: A descriptive qualitative design was employed. Semi-structured interviews were conducted with COPD patients from a rehabilitation centre in China and analysed using deductive content analysis and reported in line with COREQ guidelines. Results: Sixteen patients (mean age 71.4 years; 75% male) participated. Across 3 dimensions of self-care, 7 categories and 21 subcategories were identified. In maintenance, patients reported difficulties with lifestyle modification, physical activity, and medication adherence. In monitoring, challenges included limited symptom recognition and lack of access to monitoring devices. In management, low confidence in autonomous care and reluctance to consult providers were major barriers. Conclusion: Nurses are encouraged to assess patients’ existing barriers to self-care within their specific contexts and cultural backgrounds and to develop targeted, patient-centred nursing interventions aimed at strengthening self-care knowledge, motivation, and skills in clinical practice. Supportive policies are needed to promote the development of innovative self-care programmes.

Despite the increasing prevalence of differentiated thyroid cancer (DTC) in mainland China, the health-related quality of life (HRQoL) of DTC patients remains suboptimal. The transitional period from hospital to home is crucial for patients to adapt to their new status as survivors. However, such patients usually face several challenges and their HRQoL remains poorly understood. Therefore, this study aimed to explore the HRQoL and predictors in Chinese DTC patients during the transitional period. This single-center cross-sectional study including 300 patients was conducted in China. Patients completed the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, the Thyroid Cancer-Specific Quality of Life Questionnaire, and psychosocially relevant questionnaires. Cluster analysis, univariate logistic regression, and multivariate logistic regression analysis were used to determine the number of HRQoL clusters and predictors of HRQoL in DTC patients, respectively. The HRQoL of DTC patients during the transitional period were divided into two phenotypes: high HRQoL (69.3

ObjectiveThis study aims to identify the key risk factors for occupational exposure among oral healthcare workers and develop a predictive model using machine learning algorithms to lay the foundation for early screening of high-risk populations and the formulation of preemptive intervention plans.MethodsA multicenter cross-sectional study was conducted among 367 oral healthcare workers in 27 hospitals in Tianjin, China, from January 2025 to June 2025. Data were collected via an online questionnaire, encompassing demographic information, Work Preference Inventory, Organizational Climates, resilience, and other relevant factors. Logistic regression, random forest, decision tree, and XGBoost algorithms were employed to construct predictive models. The models were evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score.ResultsThe incidence rates of occupational exposure in the modeling and validation groups were 15.5% and 16.5%, respectively. Univariate analysis revealed significant differences between the exposed and non-exposed groups in terms of Work Preference Inventory, Organizational Climates, resilience, professional title, hospital level, age, and gender. Multivariate analysis using logistic regression indicated that Work Preference Inventory, resilience, Organizational Climates, professional title, hospital level, and gender were independent risk factors for occupational exposure. The random forest model exhibited the best predictive performance, with an AUC of 0.755, accuracy of 89.2%, sensitivity of 56.3%, specificity of 94.7%, and F1 score of 0.600.ConclusionThis study successfully identified the key risk factors for occupational exposure among oral healthcare workers and developed a predictive model using the random forest algorithm. These findings can guide the development of targeted interventions to mitigate the risks of occupational exposure. Future research should focus on validating the model with larger and more diverse datasets.

OBJECTIVE:To construct symptom networks for problematic internet use (PIU) and sleep disturbances across gender and age groups in adults, identifying core symptoms and bridging symptoms to inform targeted interventions. METHODS:We analysed cross-sectional data from the Psychology and Behaviour Investigation of Chinese Residents (PBICR 2022; N = 27,394). Problematic internet use (PIU) was assessed using the Problematic Internet Use Questionnaire-Short Form (PIUQ-SF6), and sleep quality with the Brief Pittsburgh Sleep Quality Index (B-PSQI). Regularised partial correlation networks were employed to estimate symptom relationships. Centrality and bridge centrality analyses identified core symptoms and bridging connectors. Network Comparison Tests (NCT) assessed structural differences across gender and age groups. RESULTS:In the network of relationships between PIU and B-PSQI across gender groups, both males and females exhibited the strongest positive correlation edges between B-PSQI-4 (sleep duration) and B-PSQI-5 (sleep disturbance). The most central node in each network differed: PIUQ-3 (inability to reduce use) was central in the female network, whereas PIUQ-6 (emotional dependence) was central in the male network. In the network of relationships between PIU and B-PSQI across age groups, the strongest positive correlation edge, PIUQ-2 (deprivation stress)-PIUQ-6, was identified for those aged 18-59 years old, and in which PIUQ-6 was identified as the central node. In contrast, individuals aged 60 and above exhibited the strongest positive correlation on edges B-PSQ-I4-B-PSQ-I5, with PIUQ-1 (sacrifice sleep) identified as the central node. Across gender and age groups, bedtime procrastination was identified as a bridging variable in the network. NCTs revealed that the strength of the network was significantly stronger in males, and that there were significant differences in network structure across gender and age groups. CONCLUSION:Tailored interventions are essential to extend sleep duration and curb pathological smartphone use across demographic groups, with emotional regulation strategies recommended for men, enhanced time management for women, and screen time reduction for older adults vulnerable to sleep problems.

[This corrects the article DOI: 10.3389/fpubh.2025.1713841.].

Frailty is emerging as a determinant of adverse health outcomes in older adults; identifying high-risk groups early and taking effective interventions can improve the quality of life and prognosis for the elderly. The aim of this study was to build and test a model to predict frailty among the older Chinese person, facilitating early intervention. This cross-sectional study used data from the Psychology and Behavior Investigation of Chinese Residents (PBICR), the set was split into a training set and a validation set at a 70:30 ratio. Logistic regression analyzed frailty factors, and a nomogram was developed to predict frailty, with calibration curves and decision curve analysis (DCA) assessing accuracy. 4,367 people above 60 years of age from the PBICR database in 2022 were included in the final analysis. A total of 1,190 exhibited frailty symptoms. Multivariate logistic regression analysis showed that age, BMI, history of alcohol consumption, depression, social support, family type, household location, cataract, debt, and neighborhood were predictors of frailty in older adults. These factors were used to construct the nomogram model, which showed good concordance and accuracy. The AUC values of the predictive model and the internal validation set were 0.746 and 0.711, respectively. Hosmer–Lemeshow test values were P = 0.212and P = 0.319. The calibration curves showed significant agreement between the nomogram model and actual observations. DCA indicated that the nomogram had a good predictive performance. The nomogram is a promising tool for assessing frailty risk in the older Chinese person, potentially aiding in personalized diagnosis and intervention strategies in clinical settings.

Data sharing within psychiatric and behavioral research represents a novel application of ethical principles in practice; however, it suffers from a dearth of practical experience and established ethical norms. In this study, we comprehensively examined the ethical considerations surrounding the acquisition, management, sharing, and utilization of such data. We graded sensitive data and suggest ethical standards for privacy protection based on varying levels of data sensitivity. The objective of this study is to foster orderly and standardized open sharing of psychiatric and behavioral research data, thereby advancing the development and progress of related academic disciplines in China. This Chinese expert consensus has been registered on the International Guide Registration platform (Registration Number: PREPARE-2024CN412).

With its increasing incidence, lung cancer has become one of the leading causes of cancer-related deaths worldwide, posing a great threat to the health and lives of patients. Due to varying economic and cultural backgrounds, there are significant differences in clinical treatment practices and related basic research on lung cancer between Japan and China. These differences are mainly reflected in many aspects, such as cancer prevention, cancer treatment, provision of medical insurance, patient compliance, medical education system, and sources of research funding. By understanding these differences, Japan and China can learn from each other, make progress together, and strengthen further exchanges and cooperation, which will help improve the long-term efficacy of lung cancer treatment and improve patients' clinical outcomes.

Bedtime smartphone use has become a common practice among modern individuals. The mechanisms underlying the association between bedtime smartphone use and anxiety are not fully understood in the whole population and across genders. Additionally, it remains unclear whether reducing problematic internet use (PIU) can lessen the association between bedtime smartphone use and anxiety. 30,504 subjects were recruited from the Psychology and Behavior Investigation of Chinese Residents (PBICR). Logistic regression models were used to analyze the association between bedtime smartphone use and the risk of developing anxiety, as well as the interaction effect of problematic internet use (PIU) on this association. Multiple linear regression models were conducted to analyze the association between bedtime smartphone use and the severity of anxiety symptoms. Network analysis was utilized to identify core symptoms and to distinguish gender differences in the association between bedtime smartphone use and anxiety symptoms. Compared to participants who used their smartphones for one hour or less before bedtime, using smartphones for more than one hour before bedtime was associated with a 9.1 β = 0.116, P < 0.001). In the network of bedtime smartphone use and anxiety symptoms in the general population, “Inability to stop or control worrying (GAD2)” and “Worrying too much about a variety of things (GAD3)” exhibited the highest centrality. The path coefficient between the duration of bedtime smartphone use and “Becoming annoyed or easily irritated (GAD6)” was the largest. Compared to males, the centrality of “Difficulty relaxing (GAD4)” was higher in females, and the path coefficients between the “The duration of mobile phone use before bedtime (phone)” and “Feeling nervous, anxious, or on edge (GAD1)”, “Inability to sit still due to restlessness (GAD5)”, and “Becoming annoyed or easily irritated (GAD6)” were greater in females. The centrality of “Feeling scared because something terrible seems to be about to happen (GAD7)” was higher in males. Individuals who reported both bedtime smartphone use of more than 1 h and PIU were associated with a 276.2

The effectiveness and generalisability of conventional anxiety treatment programmes is low across the population, and it is important to explore the research evidence for preventing and improving anxiety from a physical activity perspective. This study examined sedentary activity’s impact on anxiety, its dose–response relationship, and the interactive effects of 10-min walks and sedentary duration on anxiety. A total of 28,977 individuals were chosen from the Chinese Psychological and Behavioural Study of the Population (PBICR) 2022. Binary logistic regression analyzed the association between sedentary time and anxiety and the interaction effect of daily 10-min walks and sedentary time on anxiety. Restricted cubic spline model explored the dose–response relationship between sedentary time and anxiety risk. Participants who were sedentary for > 6 h had a 25.1

BACKGROUND:To construct and visualize network associations between different kinds of leisure activities and Instrumental Activities of Daily Living (IADL) levels of widowed older adults, and compare the difference in widowed elderly with different demographics. METHODS:This was a national cross-sectional study derived from CLHLS in 2018, consisting of 8594 widowed older adults. Network analysis was used to explore the relationship between different types of leisure activities and IADL. RESULTS:Male, 65-84 years old, living in urban and living alone were more likely to participate in leisure activities. LA1(Housework), LA4(Visit and interact with friends) and LA10(Watch TV/listen to radio) were associated with IADL in widowed older adults. LA1 and LA12(Square dance) were more strongly associated with IADL in women and LA9 (Play cards/mahjong) in men. LA6(Garden work) and LA8(Raise domestic animals) were strongly correlated with IADL in widowed aged 65-84 years, while LA1 and LA4 were found in aged ≥85 years. LA5(Other outdoor activity) and LA11(Social activities) had a strong correlation with IADL in urban widowed elderly people. LA1 and LA4 were strongly correlated with IADL in widowed who did not live alone, and LA8 and LA9 in widowed who live alone. CONCLUSIONS:When determining the types of leisure activities that are most needed, gender, age, place of residence, and whether or not they live alone, should be taken into account to create a supportive environment for meaningful and valuable leisure activities for widowed older persons with different characteristics to promote healthy aging.

Traditional Chinese medicine (TCM) is an ancient medical system with distinctive ethnic characteristics. TCM diagnosis, underpinned by unique theoretical frameworks and methodologies, continues to play a significant role in contemporary healthcare. The four fundamental diagnostic methods, inspection, auscultation-olfaction, inquiry and palpation, are inherently subjective, relying on practitioner experience. Despite its unique advantages and practical value, TCM must still take advantage of modern advancements to enhance its effectiveness and accessibility. With the rapid development of computer technology, intelligent TCM diagnosis has emerged as a promising frontier. Integrating artificial intelligence (AI), particularly through large language models (LLMs), offers new avenues for enhancing TCM diagnostic practices. However, the systematic review and analysis of these technologies remains limited. This paper provides a comprehensive overview of the development and recent advancements in TCM diagnostic technologies, focusing on the applications of ML across various data modalities, and including images, text, and waveforms. Additionally, it explores the latest applications of LLMs within the TCM diagnostic field. Furthermore, the review discusses the prospects and challenges associated with AI-based TCM diagnosis. By systematically summarizing the latest research achievements and technological advancements, this study aims to provide directional guidance and decision support for future research and practical applications in the intersection of AI and TCM. Ultimately, this review seeks to foster the continued development and integration of intelligent TCM diagnosis into modern healthcare.

AIMS:Explore the association between short sleep and hypertension risk in the Chinese population. METHODS:Data from the 2020 Chinese Psychological and Behavioural Study of the Population were utilised. Restricted cubic spline models assessed dose-response relationships between sleep duration and hypertension risk. A binary logistic regression model, incorporating propensity score matching, explored the true association between short sleep duration and hypertension risk in the Chinese population. In addition, using binary logistic regression models examined the association between >5 h of sleep and hypertension risks and the impact of health behaviours on hypertension risk among short sleepers. RESULTS:Sleep duration and hypertension risk exhibited a non-linear U-shaped pattern. ≤5-hour sleepers had a 32% reduced hypertension risk per additional hour of sleep post-matching. >5-hour sleep didn't affect hypertension risk. Among ≤5-hour sleepers, smoking and prolonged fixed position work increased hypertension risk by 128 and 103.4%, respectively, while engaging in physical activity for over six months reduced it by 63.7%. CONCLUSION:The 5-hour sleep threshold represents a significant turning point for hypertension risk in the Chinese population studied and could serve as a criterion for defining short sleep. Lifestyle modifications such as quitting smoking, adjusting posture during work, and maintaining regular exercise routines can mitigate hypertension risk among individuals with short sleep duration.

Sarcopenia prevalence and its risk factors in chronic obstructive pulmonary disease (COPD) vary partly due to definition criteria. This systematic review aimed to identify the prevalence and risk factors of sarcopenia in COPD patients. This review was registered in PROSPERO (CRD42022310750). Nine electronic databases were searched from inception to September 1st, 2022, and studies related to sarcopenia and COPD were identified. Study quality was assessed using a validated scale matched to study designs, and a meta-analysis was performed to evaluate sarcopenia prevalence. COPD patients with sarcopenia were compared to those without sarcopenia for BMI, smoking, and mMRC. The current meta-analysis included 15 studies, with a total of 7,583 patients. The overall sarcopenia prevalence was 29% [95% CI: 22%-37%], and the OR of sarcopenia in COPD patients was 1.51 (95% CI: 1.19-1.92). The meta-analysis and systematic review showed that mMRC (OR = 2.02, P = 0.04) and age (OR = 1.15, P = 0.004) were significant risk factors for sarcopenia in COPD patients. In contrast, no significant relationship was observed between sarcopenia and smoking and BMI. Nursing researchers should pay more attention to the symptomatic management of COPD and encourage patients to participate in daily activities in the early stages of the disease.

To provide a scoping review of studies on factors affecting smoking cessation in patients with chronic obstructive pulmonary disease (COPD), so as to provide a basis for healthcare professionals to intervene early in the process of cessation of smoking in patients with COPD, and to formulate personalized interventions for smoking cessation. Arksey and O’Malley’s scoping review methodology as a framework, searched databases including CNKI, Wanfang Data, VIP, China Biomedical Database, PubMed, Web of Science, Embase, ProQuest, CINAHL, and Cochrane Library to collect literature on factors influencing smoking cessation among COPD patients. The literature was screened, data extracted, and summarized accordingly. A total of 28 papers were included. The socio-demographic related factors affecting smoking cessation in patients with COPD were age, educational level, residence, marital status, occupational status, economic status, race, and sex; tobacco related factors included smoking index, smoking duration (years), cumulative smoking (packs/year), smoking intensity (packs/day), and tobacco addiction; disease related factors included mMRC score, GOLD level, severity of airflow restrictions, symptom, activity limitation due to lung problems, history of deterioration in outpatient care, receipt of COPD medication, receipt of lung CT, receipt of pulmonary function tests, receipt of surgery, and comorbid comorbidities; psychologically related factors included mental health status, quit smoking health beliefs, smoking cessation self-efficacy, motivation to quit smoking, stress, and adverse emotions; environmental/Interpersonal network related factors-included environmental impacts, social support, family support, tobacco control policies, and satisfaction with cessation care; and behavior related factors included alcohol consumption, coffee consumption, eating, physical activity, and have a hobby. Healthcare professionals should avoid critical education of COPD patients in the process of smoking cessation management, pay attention to the adverse effects of medication side effects on patients, emphasize the improvement of patients’ health beliefs and self-efficacy in smoking cessation, and help patients to establish a correct cognition of smoking cessation.

Background and Aim: COPD nursing plays a crucial role in alleviating disease symptoms, prolonging patient survival, and is therefore of paramount importance. However, authoritative research findings, research hotspots, and development trends in the field of COPD are still unclear. This study aimed to examine authoritative research findings, research hotspots, and trends in the field of COPD nursing. Descriptive statistics and bibliometric and visual analyses of the literature were conducted. Methods: Bibliometric data were obtained from the Web of Science database. Citespace was used to explore publication trends, countries, institutions, journals, authors, keywords, and co-citation characteristics of the included literature in order to summarize the key research in the field of COPD nursing. Results: In total, 693 articles on COPD nursing were published. 1998-2014 showed a rapid growth period in this research field, which stabilized in 2015-2022. The research content could mostly be summarized into five categories: acute exacerbation, quality of life, risk, evidence-based nursing, and pulmonary rehabilitation. The research hotspots in 1998-2014 included randomized controlled trials, education, elderly patients, nursing home residents, nursing homes, rehabilitation, and prevalence. Research in 2015-2022 focused on impact, palliative care, needs, and predictors. In recent years, research mainly concentrated on symptom management models, costeffectiveness, and cumulative meta-analysis. Conclusion: Bibliometric analysis of COPD nursing articles indicates that the focus of COPD nursing research is shifting from tertiary prevention to primary and secondary prevention. Helping patients achieve self-management of symptoms, reducing the financial burden of COPD on healthcare, and summarizing research evidence by meta-analyses will likely remain the focus of future research.