Background HIV/AIDS remains a major global health issue, and young students are a key affected group. Conventional interventions have overlooked students’ psychological and behavioural differences, reducing their impact. This study will identify specific psychosocial factors that drive college students to get HIV testing.Methods We surveyed 4451 college students in various regions in China from December 2022 to March 2023. Analysis occurred in two steps. First, we used partial least squares structural equation modelling (PLS-SEM) to test linear causal relationships among psychosocial constructs. Second, we used fuzzy set qualitative comparative analysis (fsQCA) to pinpoint combinations leading to high willingness for HIV testing.Results In PLS-SEM, lower HIV stigma (β=0.352, 95% CI 0.311 to 0.391) and higher condom-use self-efficacy (β=0.087, 95% CI 0.056 to 0.120) were associated with stronger safe-sex intention, which was associated with higher testing willingness (β=0.067, 95% CI 0.053 to 0.081). FsQCA identified five distinct pathways leading to high HIV testing willingness (coverage=0.788, consistency=0.790). The main types were (1) students with strong condom use intention; (2) students with low HIV stigma and supportive environments; (3) students with high risk perception and preventive actions; (4) students with high self-efficacy and HIV knowledge and (5) students confident in risk management and willing to test despite risk. These findings show there are multiple clear ways students reach a high willingness for HIV testing.Conclusion Using a dual analytic approach, we found that lower HIV stigma and higher condom-use self-efficacy were associated with greater safe-sex intention, which in turn was associated with higher HIV testing willingness. fsQCA further suggested multiple psychosocial configurations linked to high willingness. These findings may inform a combined universal-plus-tailored approach to campus HIV testing promotion, while longitudinal and intervention studies are needed to test causality.

Background: Comprehensive, standardized data on psychological and behavioral health among Chinese residents remain limited, constraining population-level research and the development of evidence-based health promotion strategies. Objective: The 2024 Psychology and Behavior Investigation of Chinese Residents aimed to establish a large-scale, multi-center, nationally representative database to support research on physical and mental health among Chinese residents and inform health policy development. Methods: This national cross-sectional survey was conducted from June to September 2024 using a three-stage stratified sampling strategy across 150 cities and 800 communities/villages in China. Of 38,793 distributed questionnaires, 35,861 valid responses were retained after excluding non-consenting, underage, non-Chinese, and insufficiently completed questionnaires, followed by logical quality checks (response rate: 99.05%; qualification rate: 98.95%). Trained investigators administered standardized questionnaires through face-to-face interviews, covering sociodemographic characteristics, personal health status, family information, social environment, psychological scales, behavioral scales, and attitudes toward topical social issues. Multi-level quality control procedures were implemented throughout data collection. Results: Data collection has been completed, yielding a nationally representative dataset with multidimensional information on psychological and behavioral characteristics across 150 cities. Detailed descriptions of sampling outcomes, response rates, and data quality metrics are presented in the full protocol. Conclusion: The 2024 Psychology and Behavior Investigation of Chinese Residents provides a comprehensive, high-quality evidence base to inform post-pandemic health promotion strategies and policy development aimed at improving physical and mental health in China and globally.

Background:The management of type 2 diabetes requires sustained self-management across diet, physical activity, medication adherence, and blood glucose monitoring; however, maintaining these behaviors in daily life remains difficult for many patients. Artificial intelligence-enabled and mobile health interventions have shown promise in supporting diabetes education and self-management, but evidence on how patients actually experience and use such systems in real-world primary care remains limited. Objective:This study aimed to explore patients' experiences with the Artificial Intelligence-Based Health Education Accurately Linking System (AI-HEALS) and its perceived influence on self-management behaviors among patients with type 2 diabetes. Methods:This explanatory qualitative study was nested within the intervention arm of a cluster randomized controlled trial of AI-HEALS. Purposive maximum-variation sampling was used to recruit participants who varied by sex, age, diabetes duration, hemoglobin A1c level, digital literacy, and level of platform use. Of the 25 patients approached from the intervention arm, 17 agreed to participate. Semistructured interviews were conducted 3 months after the intervention (August to December 2023) with participants recruited from 45 communities in the Daxing and Shunyi districts of Beijing, China. The qualitative component was designed to explain perceived mechanisms of behavior change, contextual facilitators and barriers, and implementation-related experiences not captured by trial outcomes alone. Interview transcripts were analyzed thematically in NVivo 12 by 2 independent researchers using consensus coding, an audit trail, and member checking. Results:Participants' experiences with AI-HEALS were reflected in four themes: (1) catalyzing health awareness and concern, (2) empowering self-management practices, (3) navigating usability and engagement, and (4) enhancing psychological adaptation. Participants perceived that AI-HEALS made diabetes more visible in daily life through repeated reminders and accessible educational content; supported practical self-management decisions related to diet, physical activity, medication adherence, and glucose monitoring; and, in some cases, improved confidence while reducing uncertainty and diabetes-related stress. The findings also suggested meaningful variation in how the intervention was experienced, particularly in relation to prior illness experience, routine stability, social support, and digital confidence. Many participants mainly engaged with lower-burden features such as pushed articles and reminders, whereas more interactive functions, such as the chatbot, were used less often. Conclusions:Patients generally perceived AI-HEALS as a useful source of ongoing education, behavioral prompting, and everyday support for diabetes self-management. The findings suggest that artificial intelligence-enabled education may work not only by increasing knowledge but also by reinforcing awareness, translating guidance into feasible daily action, and supporting psychological adaptation. At the same time, the intervention's perceived usefulness depended on whether its content was understandable, low burden, and compatible with users' routines. These results support further refinement of literacy-sensitive, layered digital interventions and their integration into routine community-based primary care.

Adolescents with diabetes face challenges in self-management and glycemic control, with traditional care offering limited support. To evaluate the effects of digital interventions on adolescents with diabetes and their feature preferences. Six English-language databases (PubMed, Web of Science, Embase, Cochrane Library, CINAHL, and PsycINFO), two Chinese-language databases (China National Knowledge Infrastructure and Wanfang), and other relevant sources were searched from inception to June 20, 2023. Randomized controlled trials involving adolescents with diabetes that evaluated digital interventions and reported at least one clinical outcome were included. Key intervention features, including parental involvement and individualization, were summarized. Nineteen studies with 1726 adolescents were analyzed. The duration and functions of digital interventions varied. Only four studies incorporated parental involvement, and none provided individualized interventions. Adolescents preferred smartphone-based interventions due to their convenience. Analysis of HbA1c at baseline, 3, 6, and 12 months showed no significant differences (Z = 1.11, P = 0.27; Z = 1.48, P = 0.14; Z = 1.41, P = 0.16; Z = 0.68, P = 0.49) with digital interventions. However, smartphone applications demonstrated a significant improvement in HbA1c level (0.46 [95

OBJECTIVE:To explore differences in the Big Five personality traits among patients with type 2 diabetes mellitus (T2DM) in relation to demographic characteristics and health education preferences and to provide evidence for personalized health education strategies. METHODS:In September 2023, a cluster sampling method was used to recruit patients with T2DM from seven community health service centers in Shandong Province, China. Data were collected using a questionnaire survey. The 10-item Big Five Inventory (BFI-10) and a self-developed health education preference questionnaire were used for the assessment. Latent profile analysis (LPA) was conducted to identify personality classifications among the patients. Chi-square tests were applied to compare differences in health education preferences across personality groups, and stratified binary Logistic regression analysis was performed to examine the association between personality classification and health education preferences. RESULTS:A total of 612 patients were included and categorized into three personality groups: Regular-balanced type (69.8%), introverted-sensitive type (6.4%), and proactive-adaptive type (23.8%). In the univariate analysis, the proportion of patients in the proactive-adaptive group who preferred peer patients as health education providers was significantly lower than that in other personality groups (χ2=6.123, P=0.047). In contrast, patients in the introverted-sensitive group showed a significantly higher preference for health education content that included psychological support than other personality groups (χ2=8.566, P=0.014). In the Logistic regression analysis, using the regular-balanced type as the reference group, proactive-adaptive patients were less likely to prefer peer patients with diabetes as health education providers (OR=0.491, 95%CI: 0.279-0.866, P=0.014). Regarding health education content, proactive-adaptive patients demonstrated a lower preference for research-frontier information (OR=0.565, 95%CI: 0.376-0.848, P=0.006). In terms of educational delivery settings, introverted-sensitive patients were less likely to prefer mobile applications as a means of receiving health education (OR=0.374, 95%CI: 0.165-0.848, P=0.019). When the proactive-adaptive type as the reference group, introverted-sensitive patients showed a significantly higher preference for psychological support content (OR=2.122, 95%CI: 1.029-4.380, P=0.042). CONCLUSION:Personality traits exhibit substantial heterogeneity among patients with T2DM, and different personality classifications are associated with distinct health education preferences. Personality traits may serve as an important reference for developing individualized health education strategies; however, their effectiveness in improving intervention outcomes requires further validation through future studies.

To explore the cross-sectional and prospective associations of perceived family support with diabetes-specific quality of life (QoL) in type 2 diabetes mellitus (T2DM) adults, and whether the baseline indirect association through depressive symptoms differed across personality profiles. In this 12-month longitudinal study, 737 T2DM patients from community health service centers in Beijing were included. They completed the follow-up and provided valid data. Family support, depressive symptoms, personality traits and diabetes-specific QoL were assessed at baseline. Family support and QoL were reassessed at 12 months. Hierarchical regression examined the prospective association of baseline family support with 12-month QoL. PROCESS Model 7 investigated baseline conditional indirect associations via depressive symptoms across personality profiles. At baseline, greater family support showed a direct association with poorer QoL after adjustment for depressive symptoms (B = 0.116, p < 0.001). In the Introverted-Stable (IS) profile, greater family support was related to fewer depressive symptoms (B = -0.023, p = 0.019), with a significant conditional indirect association with better QoL (effect = －0.046, 95

Objective:To provide empirical evidence for optimizing meal assistance policies and strengthening their role in supporting the health of older adults,this study took Shandong Province as a case to examine the policy logic and practical challenges of meal assistance services for older adults.Methods:A combination of policy tool text analysis and qualitative interviews was employed.For the policy tool analysis,59 policy documents related to meal assistance for older adults at the central and Shandong provincial levels from 2013 to 2024 were selected and categorized using a three-part framework,with policy provisions coded and analyzed as units.Qualitative interviews were conducted in two rounds,from August to October 2023 and again in January 2026,across eight urban and rural sample sites within Shandong's three major economic circles,involving 17 providers of meal assistance services for older adults;the interview data were coded and analyzed using thematic analysis.Finally,the two sets of data,including policy tools and implementation themes,were integrated through a comparative approach.Results:The development of meal assistance policies for older adults in Shandong Province can be broad-ly divided into three stages:The initial stage,the growth stage,and the deepening stage.Overall,policy tools exhibited a balanced emphasis on supply-oriented and environment-oriented instruments,while demand-oriented instruments remained relatively insufficient.Interview findings indicated that urban areas had primarily adopted a"government subsidy+market operation"model,while rural areas mainly relied on happiness homes,village collective resources,and public welfare positions to deliver meal assis-tance services.However,both urban and rural areas faced common challenges,including a mismatch be-tween financial subsidies and operational costs,a shortage of professional staff,weak motivation for social participation,insufficient expression of demand and sustained spending by older adults,and limited im-plementation capacity at the grassroots level.Meanwhile,some meal assistance sites showed early signs of health-oriented meal assistance practices,such as adjusting menus according to older adults' dietary preferences,chewing ability,and chronic disease conditions,reducing oil and salt in meal preparation,and exploring meal delivery and health education activities.Nevertheless,these efforts remained largely experience-based and fragmented,and had not yet developed into a standardised model.Conclusion:Meal assistance services for older adults are not only a livelihood initiative to ensure access to warm meals for older adults,but also a crucial entry point for promoting balanced diets,maintaining functional capa-city,and achieving healthy aging.Future efforts should focus on refining a tiered and categorized subsidy mechanism,optimizing the differentiated supply structure between urban and rural areas,enhancing demand-side mobilization and social collaboration,strengthening grassroots governance and quality super-vision,and facilitating the transition of meal assistance services from project-based provision to an institu-tionalized and sustainable model.

Background:Sustaining self-management is critical for optimizing clinical outcomes in individuals with type 2 diabetes mellitus (T2DM). Although digital health interventions (DHIs) have shown benefits for glycemic control and self-care, much of this evidence has focused on efficacy, and the behavioral mechanisms through which DHIs produce sustained effects remain insufficiently understood. Clarifying these mechanisms could inform the development of theory-driven interventions. Objective:This study examined the longitudinal behavioral pathways through which Artificial Intelligence-based Health Education Accurately Linking System, a WeChat (Tencent)-based digital health program, influences T2DM self-management, using the Extended Multi-Theory Model (MTM) of health behavior change. Methods:An explanatory sequential mixed methods prospective longitudinal cohort study was conducted among adults with T2DM (aged ≥18 y and proficient in WeChat use), recruited from 45 primary health care institutions in Beijing, China, between July 2023 and July 2024. Self-management behavior was assessed as the primary outcome using the Summary of Diabetes Self-Care Activities, and psychosocial determinants using the Extended MTM Scale and the Diabetes-related Skills Scale. Exploratory and confirmatory factor analyses assessed the construct validity of the Extended MTM Scale. Structural equation modeling examined longitudinal pathways among Artificial Intelligence-based Health Education Accurately Linking System users across baseline and 3, 6, and 12 months. For the qualitative phase, a purposive subsample was selected through maximum variation sampling based on baseline glycated hemoglobin; interviews were analyzed thematically until thematic saturation, and integrated with quantitative findings using a joint display. Results:Of the 406 enrolled participants, 391 completed baseline assessments. The Extended MTM Scale demonstrated a 6-factor, 22-item structure with excellent internal consistency (Cronbach α=0.928) and satisfactory construct validity. The structural equation modeling showed satisfactory fit (CFI=0.984, RMSEA=0.036). Changes in the social environment (β=0.23, 95% CI 0.07-0.38; P=.003) and physical environment (β=0.25, 95% CI 0.10-0.40; P=.001) at baseline, and diabetes-related skills at month 6 (β=0.16, 95% CI 0.03-0.29; P=.01), were directly associated with self-management behavior at month 12, whereas behavioral confidence and emotional transformation showed no significant direct effects. Social environment changes were indirectly associated with behavioral confidence through participatory dialogue at month 3 (β=0.20, 95% CI 0.04-0.36; P=.01; β=0.66, 95% CI 0.57-0.74; P<.001). Thematic analysis of 17 interviews identified 3 domains: environmental context, cognitive processes, and attitudes and skills. Environmental factors converged across both data strands, while qualitative data expanded on the cognitive and attitudinal processes underlying sustained self-management. Conclusions:This study is among the first to apply the Extended MTM framework to DHI-supported T2DM self-management, with environmental factors emerging as key drivers alongside selected cognitive, attitudinal, and skills-related processes. Complementing efficacy-focused research, it illuminates the psychosocial pathways underlying sustained self-management and refines the Extended MTM in digital health contexts. These insights can inform the design of theory-driven DHIs in primary care.

This study was performed to clarify the relationship between smartphone usage and depression risk among older adults. We also examined smartphone use variations, population heterogeneity, and the mechanisms behind smartphone-related depression symptoms. A total of 10,997 older adults (mean age = 84.31 years) from a national cohort (Chinese Longitudinal Healthy Longevity and Happy Family Study) in China were included. Smartphone use was measured by use status (yes/no) and contents. Among eight dichotomous questions about contents of smartphones, chatting, shopping, traveling, and entertainment were classified as routine-oriented activities. In contrast, health management, financial management, and learning were classified as functional/cognitive-oriented activities. Thus, participants were categorized into four groups, including not using a smartphone, using smartphones both for routine-oriented and functional/cognitive-oriented activities, only for routine-oriented activities, and others. Smartphone users had lower 10-item Center for Epidemiological Studies Depression Scale scores (β = -0.62; p < 0.001) and a reduced depression risk (odds ratio = 0.77; p < 0.001) compared with nonusers. Furthermore, those using smartphones for routine-oriented and functional/cognitive-oriented activities had even lower risks than those using them only for routine-oriented activities. Second, using smartphones was more sensitive to alleviating depression among older adults aged <80 years or living in rural areas. Third, mediation analyses suggested that using smartphones may be associated with depressive symptoms through communicating with children regularly. Our findings suggest that smartphone use may have a positive effect on mental health, particularly among those who engage in diverse routine-oriented and functional/cognitive-oriented activities. Smartphone benefits are more evident in younger seniors and rural residents, indicating a need for targeted interventions for these groups.

Background: As the emerging of structural imbalance characterized by surging demand and insufficient high-quality supply in China’s health management system, smart health management services become a novel measure to address this gap. Smart health management services refer to the health monitoring, assessment and intervention with support of information communication and artificial intelligence technologies. Objective: To systematically analyze the current status, needs, and influencing factors of the using of smart health management services and devices among China’s adults, thereby providing evidence support and suggestions for its development. Methods: A mixed-methods design was employed, combining quantitative and qualitative research methods. In the quantitative section, participants aged 18 years and above were selected by a stratified cluster random sampling method, their intentions and behaviors in utilizing smart health management services were analyzed by structural equation model (SEM).In the qualitative research section, 13 interviewees were selected for semi-structured, one-on-one interviews, the findings were analyzed by grounded theory coding. Quantitative and qualitative findings were integrated using an explanatory sequential mixed-methods framework. Results: A total of 2786 adults participated the questionnaire survey with a response rate of 96.07 %. Of them, 13 participants agreed to attend the semi-structured, one-on-one interviews. The main findings are as follows: (1) 37.7 % of the adult participants used smart health management devices. The use rate presents decline with increasing age, and lower use among older adults. (2)The demand for smart health management systems shows a diversified trend, and significant differences between age groups. Overall, participants believe that certain basic functions of the smart health systems, such as health monitoring, are needed, and they hope that it can answer questions raised by users. Qualitative study further revealed that participants' needs for smart health systems are in line with Maslow's Hierarchy of Needs, which includes needs at various levels from ''basic life safety and health security'' to ''active learning and self-actualization.'' Young participants prefer the support function for basic preventive activities and optimization of lifestyle; older participants then are more concerned about whether the system has practical functions for disease management. (3)The average using willingness was moderately high (62.68 ± 20.65). In the SEM, behavioral attitude emerged as the strongest predictor of willingness of use(β=0.568, P < 0.001), followed by subjective norms (β = 0.103, P < 0.001) and media motivation (β = 0.094, P < 0.001). Electronic health literacy exerted significant indirect effects on both willingness (β = 0.045, P < 0.001) and behavior (β=0.051, P < 0.001) via media motivation, while perceived behavioral control influenced them indirectly (β = 0.014 and 0.016, both P < 0.001). Living in urban areas positively affected both willingness(β = 0.056, P < 0.001) and behavior (β = 0.125, P < 0.001). Health insurance coverage significantly promoted willingness (β = 0.039, P < 0.001). (4)Qualitative findings revealed multiple barriers to using, including high costs, product quality concerns, discomfort during using, and security issues. Attitudes toward smart health management devices were polarized, positive or negative evaluations stemmed directly from experience, perceived benefits, and device intelligence level, whereas neutral users tended to discontinue use due to a lack of perceived value. In addition, personal beliefs and cultural values strongly influenced individuals’ acceptance. Conclusion: The study identified a distinct pattern of “high wish and low use” among adults regarding smart health management services. Both using behavior and demand pattern exhibited clear age-specific differences and were shaped by a number of factors. To bridge the gap between willingness to use and actual using behavior, future efforts should focus on age-appropriate design, precision implementation, and collaboration with primary care facilities, thereby enhancing adults’ capacity for actively managing their health.

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).

ObjectivesBody shape concerns significantly impact young females' psychological wellbeing. This study aimed to estimate the short-term bidirectional relationships among BMI, ideal-actual BMI gap, and body shape concern across different BMI groups, and further explore their potential non-linear associations in young Chinese females.MethodsWe conducted a longitudinal study among Chinese females aged 18 to 30 in December 2023 (T1) and April 2024 (T2). Body mass index (BMI) was calculated using the formula: weight in kilograms divided by the square of height in meters, based on self-reported data. The body shape questionnaire 8-item version C (BSQ-8C) was adopted to measure levels of body shape concern. We utilized two-time-point cross-lagged panel models (CLPMs) to investigate temporal associations among BMI, ideal-actual BMI gap, and body shape concern, and used restricted cubic spline (RCS) fitted for multiple linear regressions to explore their potential non-linear relationships.ResultsA total of 688 young females were enrolled (mean age = 21.084, SD = 2.091). The percentages of underweight, normal, and overweight-obesity were 12.2%, 66.9%, and 20.9%, respectively. In the normal and overweight-obesity groups, the ideal BMI was significantly lower than the actual BMI at baseline. Among underweight females, 44.70% expressed a desire to further reduce their BMI. For all participants, the higher the BMI at T1, the smaller the ideal-actual BMI gap at T2, which means the more the ideal value of BMI was lower than its actual value at T2. In the normal BMI group, the ideal-actual BMI gap and body shape concern negatively predicted each other. A U-shaped correlation was observed between baseline body shape concern and BMI change in the overweight group.ConclusionComplex reciprocal effects of BMI, ideal-actual BMI gap, and body shape concern existed in different BMI groups. There is an urgent need for the whole society to pay more attention to the issue of body shape concern. In particular, health educators should organize programs to promote accurate weight perception among young women, and policymakers should enhance content regulation by restricting the promotion of extreme weight loss across media platforms. This approach would help avoid the negative impact of excessive concerns about body image on mental health.

The COVID-19 vaccination is a key strategy to control the pandemic; however, complex factors, including health awareness and social cognition, influence public intention to vaccinate. The Health Belief Model (HBM) provides a theoretical framework for understanding vaccination behavior, but how Vaccination awareness (VA) dynamically moderates the relationship between HBM domains and vaccination intentions remains unclear. This study aims to compare the characteristics of different VA classifications and explore the key factors influencing their future COVID-19 vaccination intentions based on the HBM. Using three-wave longitudinal cohort data from 500 adults in mainland China, participants were divided into four groups based on VA states: persistent awareness (Group 1), early-only awareness (Group 2), late-emerging awareness (Group 3), and persistently unaware (Group 4). ANOVA, chi-square tests, and binary logistic regression were used to analyze the relationships between HBM constructs, social cues, and vaccination intentions. Group 1 (31.40

Metabolic Syndrome (MetS) is a common precursor to cardiovascular disease, diabetes and other diseases, posing a growing threat to public health systems.To enable efficient and interpretable individual risk identification, this study developed a 3-year MetS prediction model, incorporating 31 features from demographics, behaviors, anthropometrics, and dietary intake. To address class imbalance, four strategies-SMOTE, NearMiss, SMOTEENN, and class weighting-were applied at both data and algorithm levels. LightGBM was adopted as the base classifier, and Bayesian Optimization was employed for parameter search. The best results were achieved under the SMOTEENN-BO-LightGBM model, with an AUC of 0.792 and recall of 0.812. Model interpretation via SHAP highlighted BMI, waist circumference, sex, age, and blood pressure as key risk factors. This study develops a MetS prediction model that integrates data balancing, hyperparameter optimization, and interpretable analysis, without relying on laboratory-based measurements, demonstrates practical feasibility for community-level deployment, and provides decision support for MetS screening and high-risk population intervention.

BackgroundGestational diabetes mellitus (GDM) affects pregnancy outcomes and increases the risk of type 2 diabetes mellitus (T2DM) postpartum. Traditional health education interventions have shown positive effects, but adherence to lifestyle management and motivation for continued postpartum care still require improvement. This study aimed to develop a comprehensive structured program for Chinese women with GDM to improve adherence to lifestyle management and motivation for postpartum care, thereby reducing future T2DM risk.MethodsThe development of this program was divided into five steps: (1) Analysis: a summary of evidence and the analysis of needs, population characteristics, teaching content, and teaching environment; (2) Design: the design of learning objectives, teaching logic, and teaching strategies; (3) Development: the creation and development of teaching materials; (4) Implementation: the evaluation of scientific validity and rationality through an expert meeting and trial sessions; (5) Evaluation: formative and summative assessments. Finally, a pilot test was conducted to evaluate its feasibility and acceptability.ResultsLearning objectives were set in the cognitive, affective, and psychomotor domains, with a total of 189 items. The program consisted of five sessions, including: understanding GDM, nutrition and physical activity guidelines, expecting moms—are you ready, postpartum weight management, and reinforcing healthy behaviors. In addition, there was a session specifically for women starting insulin treatment, with the theme: timely insulin injection. Teaching materials included lesson plans, teaching posters, food cards, and a mother’s handbook, etc., totaling 11 items. The pilot study included eight women with GDM, all of whom expressed a positive acceptance of the program.ConclusionsThe feasibility and acceptability of the program were confirmed, and a final version was developed. It features clear objectives, detailed teaching content, engaging teaching materials, and standardized implementation processes, all of which are of significant importance for both short- and long-term management of GDM.

The inadequate body donors have restricted the development of medical sciences in some ways in mainland China. However, the investigation of potential donors may change the status quo of body donation. From July 10th to September 15th, 2021, we conducted a cross-sectional, multi-stage sampling study collected demographic data and individuals' willingness to accept body donation from 120 cities in mainland China. A stepwise linear regression analysis was adopted. 11,031 valid samples were collected for this survey. The willingness to donate body among Chinese residents averaged 53.66 points. To be specific, patients with a different number of children (1: β=-0.05; 2: β=-0.04; ≥3: β= -0.03) are less willing to donate their organs while respondents who live in an urban area (β = 0.03), have higher education level (high school or junior college: β = 0.04, such as a bachelor degree or above: β = 0.09), feel anxious (mild, moderate β = 0.02), feel pressured (moderate: β = 0.08; severe: β = 0.09), have higher scores of the Short-Form Health Literacy Instrument (HLS-SF12) (β = 0.30), Chronic Disease Self-Management Study Measures (CDSMS) (β = 0.18) and EuroQol Visual Analogue Scale (EQ-VAS) (β = 0.23), are more positive to donate. In this study, we discovered that Chinese residents' acceptance level of body donation is affected by age, gender, housing, educational levels, anxiety, pressure, social support, and health literacy, among which health literacy plays a key role in residents' attitudes towards body donation. This study firstly discusses the public acceptance of body donation through a nationwide sample around mainland China.

AIMS:To investigate the global, regional, and national burden and risk factors of type 2 diabetes mellitus (T2DM) among older adults aged ≥65 years from 1990 to 2021, with projections to 2040. MATERIALS AND METHODS:Using the Global Burden of Disease database, this study analysed global, regional, and national T2DM burden and risk factors across sex, age groups, and Socio-Demographic Index (SDI) levels. Annual percentage changes were calculated, and predictions used a Bayesian age-period-cohort model. RESULTS:Among older adults aged ≥65 years, the global age-standardized prevalence and mortality of T2DM rose by 1.9% and 0.32% per year, respectively, from 1990 to 2021. T2DM's proportion of total disability-adjusted life years (DALYs) and mortality from all diseases increased, as did its share of T2DM cases across all age groups. Mortality rose fastest in the 85-89 group (0.52% annually). High SDI regions exhibited the highest prevalence, whereas lower SDI correlated with higher mortality rates. Eastern Europe and Uzbekistan experienced the fastest DALYs growth. The global burden of T2DM in older adults is projected to continue increasing by 2040. Globally, high body mass index contributed most to T2DM burden, while high temperature and sugar-sweetened beverages (SSBs) showed the fastest-growing DALYs rates. The fastest-growing risk factor in high-SDI regions was sugar-sweetened beverages, while high temperature was the fastest-growing risk in low-SDI regions. CONCLUSIONS:Diabetes cases among older adults have tripled globally since 1990 and are projected to keep increasing. Wealthier nations face diet-related risks, while poorer regions are more affected by environmental factors. Region-specific prevention strategies are urgently needed.

OBJECTIVE:This study uses the technology acceptance model (TAM) as the theoretical framework to explore the predicting factors of intention to adopt wearable activity trackers and the actual wearing behavior among Chinese type 2 diabetes mellitus (T2DM) patients over 50 with different personality traits. METHODS:The wearable activity tracker (WAT) was freely distributed to T2DM patients recruited from 22 community health service stations affiliating to four community health service centers in Beijing. A questionnaire survey was conducted to examine predicting factors of adoption intention after a week's try-on. Actual wearing behavior for 30-day was obtained from the exclusive cloud. Latent profile analysis was used to explore personality portraits. Structural equation modeling (SEM) was used to analyze the data. RESULTS:A total of 668 patients (age over 50) with T2DM were included in the analysis. According to the latent profile analysis, the T2DM patients in this study could be classified into four personality profiles: Negative, Anxious, Introverted-Stable (IS), and Active-Responsible (AR). The results of SEM indicated that perceived ease of use (PEOU, β = 0.37, P < 0.01), perceived usefulness (PU, β = 0.31, P < 0.01), social image (SI,β = 0.11, P < 0.01), and privacy concerns (PC, β = -0.50, P < 0.01) directly influenced behavioral intention. Neuroticism positively influenced SI (β = 0.18, P < 0.01). Conscientiousness and openness positively impacted PEOU (β = 0.20, P < 0.01; β = 0.09, P < 0.05). Agreeableness negatively influenced PC (β = -0.17, P < 0.01). Openness and extraversion positively impacted individual innovation(β = 0.15, P < 0.01; β = 0.17, P < 0.01). CONCLUSIONS:Adoption intentions of WAT was the main factor influencing the actual wearing behavior of older patients with T2DM. PEOU, PU, and SI were the main facilitators of adoption intention, while PC was the main barrier. Different personality traits have their particular path of influence on WAT adoption intentions. It is recommended that future interventions with new devices or technologies for older patients with T2DM be carried out according to the preferences and needs of patients with different personality traits, such as product ease of use, innovativeness, and aesthetics, to promote patients' intention and actual use behaviors.

This article outlines the statistical methods and practical steps involved in designing and developing valid and reliable questionnaires in primary care. Based on literature review on questionnaire development and scale design, we proposed a standardized protocol for scale development in primary care. This process encompasses key practical steps and statistical methods, illustrated by cases from prior research.The recommended seven-step approach includes: (1) defining the construction of measurement; (2) generating the pool of items; (3) selecting the scoring system and response format; (4) pre-testing (assessing content validity and face validity, etc.); (5)eliminating items by item analysis; (6)evaluating the scale initially, including evaluating the reliability and validity of the scale, and factor analysis or Rasch analysis; (7)re-evaluating the scale to re-examine the nature of the scale, including retesting reliability and constructing validity. In conclusion, scale development studies should adhere to standardized procedures, and the integrated use of Rasch model and factor analysis can make the measurements more objective.

This study aimed to estimate the age, period, and cohort trends of multimorbidity among Chinese older adults from 2002 to 2022, and to explore how these three trends were affected by gender and whether or not living alone. Data were extracted from the China Longitudinal Healthy Living Survey (CLHLS) (2002–2022), and a total of 52,876 valid samples aged 65 to 105 were included. We measured 15 types of chronic diseases, and participants having two or more chronic diseases were considered to have multimorbidity. Hierarchical age–period–cohort cross-classified random-effects model (HAPC-CCREM) was applied to examine the age, period, and cohort dynamics of multimorbidity. The average age of participants is 85.91 ± 11.09 years. The temporal effect of age on multimorbidity is inverted U-shaped, with a higher probability in women aged 65–95 years and in men aged 95–105 years. The cohort trends of multimorbidity showed a rise, then a fall, then a rise again, and the period trends declined in fluctuations. Gender differences existed in age and cohort trends of multimorbidity. Moreover, the interaction of gender and living alone was significantly associated with the age trends of multimorbidity. Women living alone have a lower risk of multimorbidity than women living with others among all age groups. This study revealed the separate effects of age, period, and cohort on multimorbidity. The peaks of multimorbidity probability for different genders occur at different ages, and living alone had a protective effect on females, which both provide a scientific basis for the allocation of healthcare resources. The dynamic shifts of multimorbidity may help to forecast future multimorbidity trends and inform policies on population aging.