Investigation of factors influencing abnormal pulmonary ventilation function in occupational exposed populations and the establishment of a risk prediction model.

The purpose of this study is to investigate the influencing factors of abnormal pulmonary ventilation function in occupational exposed populations and to establish a risk prediction model. The findings will provide a basis for formulating corresponding strategies for the prevention and treatment of occupational diseases. The study focused on workers who underwent occupational health examinations in the year 2020. Statistical analysis was conducted using methods such as t-tests, chi-square tests, and multiple logistic regression analysis. Additionally, machine learning methods were employed to establish multiple models to address classification problems. Among the 7472 workers who participated in the occupational health examination, 1681 cases of abnormal pulmonary ventilation function were detected, resulting in a detection rate of 22.6%. Based on the analysis of occupational hazard data, a risk prediction model was established. Age, work tenure, type of the employing enterprise, and type of dust exposure are all identified as driving factors for abnormal pulmonary function. These factors were used as predictive variables for establishing the risk prediction model. Among the various models evaluated, the logistic regression model was found to be the optimal model for predicting abnormal pulmonary ventilation function.

Relation extraction using large language models: a case study on acupuncture point locations.

OBJECTIVE: In acupuncture therapy, the accurate location of acupoints is essential for its effectiveness. The advanced language understanding capabilities of large language models (LLMs) like Generative Pre-trained Transformers (GPTs) and Llama present a significant opportunity for extracting relations related to acupoint locations from textual knowledge sources. This study aims to explore the performance of LLMs in extracting acupoint-related location relations and assess the impact of fine-tuning on GPT's performance. MATERIALS AND METHODS: We utilized the World Health Organization Standard Acupuncture Point Locations in the Western Pacific Region (WHO Standard) as our corpus, which consists of descriptions of 361 acupoints. Five types of relations ("direction_of", "distance_of", "part_of", "near_acupoint", and "located_near") (n = 3174) between acupoints were annotated. Four models were compared: pre-trained GPT-3.5, fine-tuned GPT-3.5, pre-trained GPT-4, as well as pretrained Llama 3. Performance metrics included micro-average exact match precision, recall, and F1 scores. RESULTS: Our results demonstrate that fine-tuned GPT-3.5 consistently outperformed other models in F1 scores across all relation types. Overall, it achieved the highest micro-average F1 score of 0.92. DISCUSSION: The superior performance of the fine-tuned GPT-3.5 model, as shown by its F1 scores, underscores the importance of domain-specific fine-tuning in enhancing relation extraction capabilities for acupuncture-related tasks. In light of the findings from this study, it offers valuable insights into leveraging LLMs for developing clinical decision support and creating educational modules in acupuncture. CONCLUSION: This study underscores the effectiveness of LLMs like GPT and Llama in extracting relations related to acupoint locations, with implications for accurately modeling acupuncture knowledge and promoting standard implementation in acupuncture training and practice. The findings also contribute to advancing informatics applications in traditional and complementary medicine, showcasing the potential of LLMs in natural language processing.

Changing Benzoxazole Ring into Nonring Imine Linkages on Covalent Organic Frameworks with Tuning H2O2 Photosynthesis Performance.

Two π-conjugated covalent organic frameworks (COFs) with nonring imine or benzoxazole ring linkages were prepared by reacting 3,3'-dihydrooxybenzidine (BDOH) with 3,5-triformylbenzene (Tb) in the presence or absence of benzimidazole (BDOH-Tb-IM and BDOH-Tb-BO). Although two COFs indicated similar composition, crystalline structures, and morphologies, imine-based BDOH-Tb-IM exhibited a photocatalytic H2O2 production rate of 2490 µmol·g-1·h-1 in sacrificial reagent-free pure water, higher than that of benzoxazole-based BDOH-Tb-BO-a (1168 µmol·g-1·h-1). The higher photocatalytic activity of BDOH-Tb-IM was attributed to its more efficient photoinduced charge separation and utilization efficiency and different 2e- ORR active sites over the two COFs. This study demonstrated an available ring effect to adjust photocatalytic performance between π-conjugated COFs.

A Study of Biomedical Relation Extraction Using GPT Models.

Relation Extraction (RE) is a natural language processing (NLP) task for extracting semantic relations between biomedical entities. Recent developments in pre-trained large language models (LLM) motivated NLP researchers to use them for various NLP tasks. We investigated GPT-3.5-turbo and GPT-4 on extracting the relations from three standard datasets, EU-ADR, Gene Associations Database (GAD), and ChemProt. Unlike the existing approaches using datasets with masked entities, we used three versions for each dataset for our experiment: a version with masked entities, a second version with the original entities (unmasked), and a third version with abbreviations replaced with the original terms. We developed the prompts for various versions and used the chat completion model from GPT API. Our approach achieved a F1-score of 0.498 to 0.809 for GPT-3.5-turbo, and a highest F1-score of 0.84 for GPT-4. For certain experiments, the performance of GPT, BioBERT, and PubMedBERT are almost the same.

Me-LLaMA: Foundation Large Language Models for Medical Applications.

Recent advancements in large language models (LLMs) such as ChatGPT and LLaMA have hinted at their potential to revolutionize medical applications, yet their application in clinical settings often reveals limitations due to a lack of specialized training on medical-specific data. In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets. Our methodology leverages a comprehensive domain-specific data suite, including a large-scale, continual pre-training dataset with 129B tokens, an instruction tuning dataset with 214k samples, and a new medical evaluation benchmark (MIBE) across six critical medical tasks with 12 datasets. Our extensive evaluation using the MIBE shows that Me-LLaMA models achieve overall better performance than existing open-source medical LLMs in zero-shot, few-shot and supervised learning abilities. With task-specific instruction tuning, Me-LLaMA models outperform ChatGPT on 7 out of 8 datasets and GPT-4 on 5 out of 8 datasets. In addition, we investigated the catastrophic forgetting problem, and our results show that Me-LLaMA models outperform other open-source medical LLMs in mitigating this issue. Me-LLaMA is one of the largest open-source medical foundation LLMs that use both biomedical and clinical data. It exhibits superior performance across both general and medical tasks compared to other open-source medical LLMs, rendering it an attractive choice for medical AI applications. We release our models, datasets, and evaluation scripts at: https://github.com/BIDS-Xu-Lab/Me-LLaMA.

mNFE: microbiome network flow entropy for detecting pre-disease states of type 1 diabetes.

In the development of Type 1 diabetes (T1D), there are critical states just before drastic changes, and identifying these pre-disease states may predict T1D or provide crucial early-warning signals. Unlike gene expression data, gut microbiome data can be collected noninvasively from stool samples. Gut microbiome sequencing data contain different levels of phylogenetic information that can be utilized to detect the tipping point or critical state in a reliable manner, thereby providing accurate and effective early-warning signals. However, it is still difficult to detect the critical state of T1D based on gut microbiome data due to generally non-significant differences between healthy and critical states. To address this problem, we proposed a new method - microbiome network flow entropy (mNFE) based on a single sample from each individual - for detecting the critical state before seroconversion and abrupt transitions of T1D at various taxonomic levels. The numerical simulation validated the robustness of mNFE under different noise levels. Furthermore, based on real datasets, mNFE successfully identified the critical states and their dynamic network biomarkers (DNBs) at different taxonomic levels. In addition, we found some high-frequency species, which are closely related to the unique clinical characteristics of autoantibodies at the four levels, and identified some non-differential 'dark species' play important roles during the T1D progression. mNFE can robustly and effectively detect the pre-disease states at various taxonomic levels and identify the corresponding DNBs with only a single sample for each individual. Therefore, our mNFE method provides a new approach not only for T1D pre-disease diagnosis or preventative treatment but also for preventative medicine of other diseases by gut microbiome.

Advancing entity recognition in biomedicine via instruction tuning of large language models.

MOTIVATION: Large Language Models (LLMs) have the potential to revolutionize the field of Natural Language Processing, excelling not only in text generation and reasoning tasks but also in their ability for zero/few-shot learning, swiftly adapting to new tasks with minimal fine-tuning. LLMs have also demonstrated great promise in biomedical and healthcare applications. However, when it comes to Named Entity Recognition (NER), particularly within the biomedical domain, LLMs fall short of the effectiveness exhibited by fine-tuned domain-specific models. One key reason is that NER is typically conceptualized as a sequence labeling task, whereas LLMs are optimized for text generation and reasoning tasks. RESULTS: We developed an instruction-based learning paradigm that transforms biomedical NER from a sequence labeling task into a generation task. This paradigm is end-to-end and streamlines the training and evaluation process by automatically repurposing pre-existing biomedical NER datasets. We further developed BioNER-LLaMA using the proposed paradigm with LLaMA-7B as the foundational LLM. We conducted extensive testing on BioNER-LLaMA across three widely recognized biomedical NER datasets, consisting of entities related to diseases, chemicals, and genes. The results revealed that BioNER-LLaMA consistently achieved higher F1-scores ranging from 5% to 30% compared to the few-shot learning capabilities of GPT-4 on datasets with different biomedical entities. We show that a general-domain LLM can match the performance of rigorously fine-tuned PubMedBERT models and PMC-LLaMA, biomedical-specific language model. Our findings underscore the potential of our proposed paradigm in developing general-domain LLMs that can rival SOTA performances in multi-task, multi-domain scenarios in biomedical and health applications. AVAILABILITY AND IMPLEMENTATION: Datasets and other resources are available at https://github.com/BIDS-Xu-Lab/BioNER-LLaMA.

Improving large language models for clinical named entity recognition via prompt engineering.

IMPORTANCE: The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets. OBJECTIVES: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance. MATERIALS AND METHODS: We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, and tests from clinical notes in the MTSamples corpus, following the 2010 i2b2 concept extraction shared task, and (2) to identify nervous system disorder-related adverse events from safety reports in the vaccine adverse event reporting system (VAERS). To improve the GPT models' performance, we developed a clinical task-specific prompt framework that includes (1) baseline prompts with task description and format specification, (2) annotation guideline-based prompts, (3) error analysis-based instructions, and (4) annotated samples for few-shot learning. We assessed each prompt's effectiveness and compared the models to BioClinicalBERT. RESULTS: Using baseline prompts, GPT-3.5 and GPT-4 achieved relaxed F1 scores of 0.634, 0.804 for MTSamples and 0.301, 0.593 for VAERS. Additional prompt components consistently improved model performance. When all 4 components were used, GPT-3.5 and GPT-4 achieved relaxed F1 socres of 0.794, 0.861 for MTSamples and 0.676, 0.736 for VAERS, demonstrating the effectiveness of our prompt framework. Although these results trail BioClinicalBERT (F1 of 0.901 for the MTSamples dataset and 0.802 for the VAERS), it is very promising considering few training samples are needed. DISCUSSION: The study's findings suggest a promising direction in leveraging LLMs for clinical NER tasks. However, while the performance of GPT models improved with task-specific prompts, there's a need for further development and refinement. LLMs like GPT-4 show potential in achieving close performance to state-of-the-art models like BioClinicalBERT, but they still require careful prompt engineering and understanding of task-specific knowledge. The study also underscores the importance of evaluation schemas that accurately reflect the capabilities and performance of LLMs in clinical settings. CONCLUSION: While direct application of GPT models to clinical NER tasks falls short of optimal performance, our task-specific prompt framework, incorporating medical knowledge and training samples, significantly enhances GPT models' feasibility for potential clinical applications.

MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy.

Complex biological systems often undergo sudden qualitative changes during their dynamic evolution. These critical transitions are typically characterized by a catastrophic progression of the system. Identifying the critical point is critical to uncovering the underlying mechanisms of complex biological systems. However, the system may exhibit minimal changes in its state until the critical point is reached, and in the face of high throughput and strong noise data, traditional biomarkers may not be effective in distinguishing the critical state. In this study, we propose a novel approach, mutual information weighted entropy (MIWE), which uses mutual information between genes to build networks and identifies critical states by quantifying molecular dynamic differences at each stage through weighted differential entropy. The method is applied to one numerical simulation dataset and four real datasets, including bulk and single-cell expression datasets. The critical states of the system can be recognized and the robustness of MIWE method is verified by numerical simulation under the influence of different noises. Moreover, we identify two key transcription factors (TFs), CREB1 and CREB3, that regulate downstream signaling genes to coordinate cell fate commitment. The dark genes in the single-cell expression datasets are mined to reveal the potential pathway regulation mechanism.

Information-Motivation-Behavioral Skills Model Supplemented With the Moderated-Mediation Path: A Framework for Interpreting Patients' Online Medical Services Utilization.

PURPOSE: This study aimed to explore the internal determinants affecting patients' utilization of online medical services (OMS) based on the information-motivation-behavioral skills model from a behavioral perspective. DESIGN: A cross-sectional study. SETTING: This study was conducted in three medical institutions in Jiangsu Province, China. SUBJECTS: 470 internet users were enrolled from patients who came to the outpatient clinics. MEASURES: A self-administered questionnaire with feasible reliability and validity was used to investigate the demographic characteristics and OMS utilization-related information, motivation, behavioral skills, intention, and behavior. ANALYSIS: According to the constructed framework, structural equation modeling was used to test the relationships between those factors and OMS utilization behaviors. RESULTS: All direct paths are established except the path between information and intention. Information and motivation positively affected OMS utilization behavior through behavioral skills and intention (P < .001). Motivation and behavioral skills could positively influence OMS utilization behavior through intention (P < .01). Motivation was found to be the largest predictor of OMS utilization behavior. Moreover, gender played a moderating role in the interpretation of the behavior. CONCLUSIONS: Interventions should be conducted regarding information, motivation, and behavioral skills to promote patients' use of OMS. At the same time, the impact of gender on intervention effectiveness should also be considered.

Weakly Hydrophilic Imine-Linked Covalent Benzene-Acetylene Frameworks for Photocatalytic H2O2 Production in the Two-Phase System.

Conversing oxygen (O2) to hydrogen peroxide (H2O2) driven by solar energy is a promising H2O2 onsite production route but often short of efficient and durable photocatalysts. Herein, strong π-π conjugate polycyclic aromatic benzene and acetylene units have been constructed into new covalent organic frameworks (COFs) linked by imine C═N bonding. These COFs demonstrated two-dimensional hexagonal crystalline frameworks with higher crystallinity and larger surface area (>600 m2 g-1). Covalent benzene-acetylene frameworks possessed appropriate visible light-responsive band structure and the suppressed charge recombination rate. The -OH groups on their frameworks enable them to be weakly hydrophilic. As a result, it served as high-performance but durable photocatalysts for H2O2 production in the water-benzyl alcohol (BA) two-phase system. It delivered a H2O2 production rate of 1240 µmol h-1 gcat-1 and durable catalytic efficiency within 60 h, comparable to the best COF-based catalysts. This study provides an efficient two-phase photocatalytic system for H2O2 production based on weakly hydrophilic imine-linked benzene-acetylene organic photocatalysts.

Behavioral Interpretation of Willingness to Use Wearable Health Devices in Community Residents: A Cross-Sectional Study.

Wearable health devices (WHDs) have become increasingly advantageous in long-term health monitoring and patient management. However, most people have not yet benefited from such innovative technologies, and the willingness to accept WHDs and their influencing factors are still unclear. Based on two behavioral theories: the theory of planned behavior (TPB) and the diffusion of innovation (DOI), this study aims to explore the influencing factors of willingness to use WHDs in community residents from the perspective of both internal and external factors. A convenience sample of 407 community residents were recruited from three randomly selected Community Health Service Centers (CHSCs) in Nanjing, China, and were investigated with a self-developed questionnaires. The mean score of willingness to use WHDs was 17.00 (range 5-25). In the dimensions of TPB, perceived behavioral control (ß = 1.979, p < 0.001) was the strongest influencing factor. Subjective norms (ß = 1.457, p < 0.001) and attitudes (ß = 0.651, p = 0.016) were also positively associated with willingness. In innovation characteristics of DOI, compatibility (ß = 0.889, p < 0.001) and observability (ß = 0.576, p = 0.003) had positive association with the willingness to wear a WHD. This study supports the applicability of the two behavioral theories to interpret the willingness to use WHDs in Chinese community residents. Compared with the innovative features of WHDs, individual cognitive factors were more critical predictors of willingness to use.

Empirical validation of the information-motivation-behavioral skills model of gestational weight management behavior: a framework for intervention.

BACKGROUND: Unhealthy gestational weight gain is a modifiable risk factor for adverse maternal and child health. Appropriate and effective intervention strategies that focus on behavioral change or maintenance are critical in weight management during pregnancy. Our aim was to uncover the influencing factors and psychosocial mechanisms of gestational weight control behavior, and to construct a behavioral model suitable for intervention based on Information-Motivation-Behavioral skills (IMB) model. METHODS: A sample of 559 pregnant women from a municipal maternal and child healthcare facility in Jiangsu Province, China was enrolled in this cross-sectional empirical study. Partial least square structural equation modelling was used to verify the hypothesized model, and post hoc analyses was used to test the effect of parity and pre-pregnancy BMI on the model. RESULTS: The IMB model elements can predict gestational weight management (GWM) behavior well, with information being the most influential factor. As predicted, information affects GWM directly (ß = 0.325, p < 0.05) and indirectly (ß = 0.054, p < 0.05) through behavioral skills. Likewise, motivation has direct (ß = 0.461, p < 0.05) effects on GWM, and has indirect (ß = 0.071, p < 0.05) effects through behavioral skills. Behavioral skills have a direct impact (ß = 0.154, p < 0.05). The model had a goodness of fit (GOF = 0.421) and was robust when tested in subgroups of different parity or pre-pregnancy BMI. CONCLUSION: Findings from this study supported the predictions of the IMB model for GWM behavior, and identified its modifiable determinants. The tested behavior model for GWM can serve as a new validated intervention strategy in weight management among pregnant women.

Specific network information gain for detecting the critical state of colorectal cancer based on gut microbiome.

There generally exists a critical state or tipping point from a stable state to another in the development of colorectal cancer (CRC) beyond which a significant qualitative transition occurs. Gut microbiome sequencing data can be collected non-invasively from fecal samples, making it more convenient to obtain. Furthermore, intestinal microbiome sequencing data contain phylogenetic information at various levels, which can be used to reliably identify critical states, thereby providing early warning signals more accurately and effectively. Yet, pinpointing the critical states using gut microbiome data presents a formidable challenge due to the high dimension and strong noise of gut microbiome data. To address this challenge, we introduce a novel approach termed the specific network information gain (SNIG) method to detect CRC's critical states at various taxonomic levels via gut microbiome data. The numerical simulation indicates that the SNIG method is robust under different noise levels and that it is also superior to the existing methods on detecting the critical states. Moreover, utilizing SNIG on two real CRC datasets enabled us to discern the critical states preceding deterioration and to successfully identify their associated dynamic network biomarkers at different taxonomic levels. Notably, we discovered certain 'dark species' and pathways intimately linked to CRC progression. In addition, we accurately detected the tipping points on an individual dataset of type I diabetes.

Association between gestational depression and weight management behaviors during pregnancy: A cross-sectional study in Eastern China.

An undesirable psychological state may deteriorate individual's weight management-related behaviors. This study aims to see if ineffective weight control measures were linked to depressive symptoms during pregnancy. We conducted a cross-sectional questionnaire survey of 784 pregnant women and collected information on sociodemographic factors, maternal characteristics, depression, and weight management activities throughout pregnancy (exercise management, dietary management, self-monitoring regulation, and management objectives). About 17.5% of pregnant women exhibited depressive symptoms. The mean score on dietary management was upper-middle, exercise management and self-monitoring regulation were medium, and management objectives were lower-middle. Multivariable linear regression analysis revealed that pregnant women with depressive symptoms had lower levels of exercise management (ß = -1.585, p = 0.005), dietary management (adjusted ß = -0.984, p = 0.002), and management objectives (adjusted ß = -0.726, p = 0.009). However, there was no significant relationship between depressive symptoms and pregnant women's self-monitoring regulating behavior (p > 0.05). The findings indicated the inverse association between depressive symptoms and gestational weight management behaviors. These results offer important indications for pregnancy weight management professionals by highlighting the need for mental health interventions for pregnant women experiencing depressive symptoms.

Behavioral Intention and Its Predictors toward COVID-19 Booster Vaccination among Chinese Parents: Applying Two Behavioral Theories.

The booster vaccination of COVID-19 is being implemented in most parts of the world. This study used behavioral psychology to investigate the predictors of parents' intentions regarding the COVID-19 booster vaccination for their children. This is a cross-sectional study with a self-designed questionnaire based on two behavioral theories-protective motivation theory (PMT) and theory of planned behavior (TPB). A stratified multi-stage sampling procedure was conducted in Nanjing, China, and multivariable regression analyses were applied to examine the parents' intentions. The intention rate was 87.3%. The response efficacy (ORa = 2.238, 95% CI: 1.360-3.682) and response cost (ORa = 0.484, 95% CI: 0.319-0.732) in the PMT, were significant psychological predictors of parents' intentions, and so were the attitude (ORa = 2.619, 95% CI: 1.480-4.636) and behavioral control (ORa = 3.743, 95% CI: 2.165-6.471) in the TPB. The findings of crucial independent predictors in the PMT and TPB constructs inform the evidence-based formulation and implementation of strategies for booster vaccination in children.

Determinants of excessive gestational weight gain: a systematic review and meta-analysis.

BACKGROUND: The prevalence of excessive gestational weight gain (EGWG) during pregnancy is increasing, and it is extremely harmful to pregnant women and newborns. Previous studies have suggested that EGWG is associated with various factors. We conducted a systematic review and meta-analysis to identify, quantify and analyze determinants of EGWG and evaluate the effect of these determinants on EGWG. METHODS: We searched for articles, from January 2009 to November 2020, related to the determinants of EGWG during pregnancy using four Chinese and four English databases. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement was utilized to guide the systematic review and meta-analysis process. RESULTS: Seventy studies, which identified EGWG factors in pregnant women (58 factors, 3 themes: individual [7 aspects, 37 factors]; family [4 aspects, 8 factors]; and social [4 aspects, 13 factors]), were included and analyzed in the systematic review. A meta-analysis was conducted for 13 factors (including 10 individual factors, 2 family factors, and 1 social factor) and revealed that pre-pregnancy overweight (including obesity), younger age (≤ 30 years old), unemployed, primiparity, smoking, and being unmarried (including divorced) were risk factors for EGWG, while prepregnancy underweight and inadequate antenatal care were protective factors for EGWG. There was no significant correlation between EGWG and education level, alcohol consumption, planning pregnancy, food security, and whether access to nutrition guidance during pregnancy. CONCLUSIONS: EGWG was prevalent in pregnant women, and its prevalence seemed to be high and similar in many countries. Based on observational studies with medium-level and high-level evidence, some individual, family, and social factors were found to be associated with EGWG using qualitative and quantitative methods. In the future, exposure of pregnant women to risk factors for EGWG should be avoided, and interventions should be developed around the identified factors.

Analysis of the Weight Management Behavior of Chinese Pregnant Women: An Integration of the Protection Motivation Theory and the Information-Motivation-Behavioral Skills Model.

Inappropriate gestational weight gain has become a public health concern that threatens maternal and child health. Pregnant women's ability to manage their weight during pregnancy directly impacts their weight gain. In this study, we integrated the protection motivation theory and the information-motivation-behavioral skills model to develop an integrative theoretical model suitable for pregnancy weight management and reveal significant explainable factors of weight management behaviors during pregnancy. Based on a cross-sectional survey of 550 pregnant women from Jiangsu province, we came up with our findings. The results showed that several factors influenced pregnancy weight management behavior. According to the research, information, self-efficacy, response costs, and behavioral skills were significantly associated with weight management behaviors during pregnancy, while behavioral skills were also significant mediators of information, self-efficacy, and behavior. Furthermore, the information related to pregnancy weight management had the biggest impact on weight management behavior during pregnancy. The results of the model fit were acceptable and the integrative model could explain 30.6% of the variance of weight management behavior during pregnancy, which implies that the integrative theoretical model can effectively explain and predict weight management behaviors during pregnancy. Our study provides practical implications for the integrative model in improving pregnancy weight management behavior and offers a theoretical base for the weight management of pregnant women.

Cognitive Factors of Weight Management During Pregnancy Among Chinese Women: A Study Applying Protective Motivation Theory.

PURPOSE: This study aimed to explore the psychological cognitive factors of weight management during pregnancy based on protective motivation theory (PMT). DESIGN: Cross-sectional study. SETTING: Participants were recruited at the Maternal and Child Health Hospital of Changzhou City, Jiangsu Province, China. SAMPLE: A sample of 533 pregnant women was enrolled in the study. MEASURES: Measures was a self-design questionnaire, comprising of demographics, cognition of weight management during pregnancy, and weight management behavior during pregnancy. ANALYSIS: Structural equation modeling was used to examine the weight management's cognitive factors, path relationships, and the influence of maternal characteristics. RESULTS: Self-efficacy cognition could promote gestational weight management behavior (b = .22, P < .001), but response cost cognition hindered gestational weight management (b = -.21, P < .001). Parity moderated pregnant women's self-efficacy cognition (diff b = .24, P < .01), where the self-efficacy of nullipara promoted weight management behaviors, but the self-efficacy of multipara had no significant effect. Also, the response cost factors stably existed in primipara and multipara groups, with multipara, being positively affected by response efficacy (b = .15, P < .05). CONCLUSION: Findings highlight the need for psychological and cognitive interventions. Intervention strategies that focus on enabling women to correctly understand response cost and make an active response, improve self-efficacy cognition especially among primipara, and strengthening multipara's response efficacy among pregnant are required.

Behaviour model integrated by protection motivation theory and information-motivation-behavioural skills model applying in pregnancy weight management (PrInMAMa): a study protocol for a randomised controlled trial in China.

INTRODUCTION: Excessive gestational weight gain poses a significant threat to maternal and child health. The healthy behaviour theory has been increasingly applied to weight management during pregnancy, but research is still insufficient. The successful application of the protection motivation theory (PMT) and the information-motivation-behavioural skills (IMB) model in the field of healthy behaviour laid the foundation for this intervention study. The overall aim of this study is to test the effectiveness of interventions based on the behaviour model integrated with the PMT and IMB model (PMT-IMB model) on weight management and provide feasible methods for weight management during pregnancy. METHODS AND ANALYSIS: This prospective, single-centre, randomised controlled trial involves two steps. First, based on the PMT-IMB model, evaluation tools and intervention materials will be developed. Second, more than 800 women in the first trimester of pregnancy will be randomly assigned to two groups and will be followed until 1 week after delivery. The control group will receive standardised antenatal care (ANC), whereas the experimental group will receive both standardised ANC and interventions based on the PMT-IMB model. After three surveys (at enrolment, at 28 weeks of gestation, and on the day of hospitalisation for delivery), primary outcomes (scores of the subscales of the PMT-IMB model, scores of the pregnancy weight management strategy scale, and gestational weight gain) and secondary outcomes (pregnancy outcomes and pregnancy complications) will be obtained. Differences in outcomes between the two groups will be analysed to evaluate the effectiveness of the intervention. ETHICS AND DISSEMINATION: The study protocol has been approved by the ethics committee of Nanjing Medical University. All participants will sign an informed consent form prior to enrolment. The findings of the study will be published in peer-reviewed journals and presented at conferences. TRIAL REGISTRATION NUMBER: ChiCTR2100043231.