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In order to meet the needs of the human-computer interaction experience of health testing products and improve the decision-making efficiency of intelligent decision support systems, we visualized the design of health testing products. We summarized the design methods for the human-computer interaction experience of health testing products, analyzed health testing data visualization requirements in terms of thematic databases, data visualization diagrams, thematic dashboards and knowledge management systems, and introduced the general process of monitoring information visualization. The visual health testing product information display interface is designed to visualize the testing data in three aspects: information architecture, interaction mode and visual language presentation. The visual intelligent decision support system and the visual interface design are combined for the functional design of the visual intelligent decision support system. The experimental part of the study investigates the effectiveness of the visualized health testing product of the intelligent decision support system, using the questionnaire method and health data measurement method to collect results on the interactivity, convenience, health decision accuracy and product satisfaction of the health monitoring product, with the data presented as a percentage system. The experimental results show that the interactivity, convenience and health decision accuracy of the intelligent decision support visual health monitoring product are higher than those of traditional health monitoring products, with interactivity evaluation results above 85% and high satisfaction with product use, indicating that the product can provide new and innovative design ideas in home healthcare.

Polycystic ovary syndrome (PCOS) is a common gynecological endocrine disorder in women of reproductive age that seriously affects both their physical and mental health. The pathogenesis of PCOS is complex and not yet fully understood, and it is crucial to control for bias and analyze the risk factors for its development in order to provide a basis for developing preventive strategies. A case–control study design was used. Patients first diagnosed with PCOS from January 2024 to June 2024 at the First Affiliated Hospital of the University of South China, the Second Affiliated Hospital of the University of South China, and the Affiliated Nanhua Hospital of University of South China were selected as the case group (n = 210). Non-PCOS women attending during the same period were selected as the control group (n = 420). Information was collected using self-administered questionnaires, including the Pittsburgh Sleepiness Scale (PSQI), the Generalized Anxiety Disorder 7 (GAD-7) scale, and the Patient Health Questionnaire (PHQ-9). A directed acyclic graph was used for variable screening. Propensity score matching controlled for confounding variables, and multifactorial logistic regression analysis identified risk factors for PCOS. Multifactorial logistic regression analysis showed that obesity [ OR  = 4.088, 95% CI (2.580, 6.476), P  < 0.001], alcohol consumption [ OR  = 2.305, 95% CI (1.320, 4.024), P  = 0.003], family history of PCOS [ OR  = 6.468, 95% CI (1.986, 21.067), P  = 0.002], low birth weight [ OR  = 0.637, 95% CI (0.438, 0.927), P  = 0.018], and anxiety [ OR  = 4.905, 95% CI (2.768, 8.693), P  < 0.001] were risk factors for PCOS development. BMI ≥ 25 kg/m 2 , alcohol consumption, family history of PCOS, low birth weight, and anxiety are risk factors for the development of PCOS. Targeted measures should be implemented to address these factors, reducing the incidence of PCOS and promoting female reproductive endocrine health.

Gestational diabetes mellitus (GDM) is a common complication during pregnancy, with its incidence increasing year by year. It poses numerous adverse health effects on both mothers and newborns. Accurate prediction of GDM can significantly improve patient prognosis. In recent years, artificial intelligence (AI) algorithms have been increasingly used in the construction of GDM prediction models. However, there is still no consensus on the most effective algorithm or model. This study aimed to evaluate and compare the performance of existing GDM prediction models constructed using AI algorithms and propose strategies for enhancing model generalizability and predictive accuracy, thereby providing evidence-based insights for the development of more accurate and effective GDM prediction models. A comprehensive search was conducted across PubMed, Web of Science, Cochrane Library, EMBASE, Scopus, and OVID, covering publications from the inception of databases to June 1, 2025, to include studies that developed or validated GDM prediction models based on AI algorithms. Study selection, data extraction, and risk of bias assessment using the Prediction Model Risk of Bias Assessment Tool were performed independently by 2 reviewers. A bivariate mixed-effects model was used to summarize sensitivity and specificity and to generate a summary receiver operating characteristic (SROC) curve, calculating area under the curve (AUC). The Hartung-Knapp-Sidik-Jonkman method was further used to adjust for the pooled sensitivity and specificity. Between-study standard deviation (τ) and variance (τ²) were extracted from the bivariate model to quantify absolute heterogeneity. The Deek test was used to evaluate small-study effects among included studies. Additionally, subgroup analysis and meta-regression were conducted to compare the performance differences among algorithms and to explore sources of heterogeneity. Fourteen studies reported on the predictive value for AI algorithms for GDM. After adjustment with the Hartung-Knapp-Sidik-Jonkman method, the pooled sensitivity and specificity were 0.78 (95% CI 0.69-0.86; τ=0.15, τ2=0.02; PI 0.47-1.09) and 0.85 (95% CI 0.78-0.92; τ=0.11, τ2=0.01; PI 0.59-1.11), respectively. The SROC curve showed that the AUC for predicting GDM using AI algorithms was 0.94 (95% CI 0.92-0.96), indicating a strong predictive capability. Deek test (P=.03) and the funnel plot both showed clear asymmetry, suggesting the presence of small-study effects. Subgroup analysis showed that the random forest algorithm exhibited the highest sensitivity (0.83, 95% CI 0.74-0.93), while the extreme gradient boosting algorithm exhibited the highest specificity (0.82, 95% CI 0.77-0.87). Meta-regression further revealed an evaluation in predictive accuracy in prospective study designs (regression coefficient=2.289, P=.001). Unlike previous narrative reviews, this systematic review innovatively provided a comparative and quantitative synthesis of AI algorithms for GDM prediction. This established an evidence-based framework to guide model selection and identified a critical evidence gap. The key implication for real-world application was the demonstrated necessity of local validation before clinical adoption. Therefore, future work should focus on large-scale, prospective validation studies to develop clinically applicable tools.

Background Experiencing internship shapes nursing students’ professional commitment and aggravates its changes. However, few studies have been investigated how this changes empirically. Objectives The aims of this study are to investigate (a) what are the changes of professional commitment of nursing students before and after the internship? (b) Which of multiple independent variables is the strongest predictor? Methods A longitudinal study was conducted with 996 senior undergraduate nursing students (ready to enter clinical practice) in the China universities. The survey was conducted in the spring of 2015 and autumn of 2016. The data were collected by a paper-and-pencil questionnaire. The instruments used included Professional Commitment Scale and Perceived Stress Scale. Analysis of paired t -test and linear regression analysis were performed on the data. Results Nursing students showed lower professional commitment (2.79 ± 0.36) than they were (2.92 ± 0.36) before internship. Socio-demographic variables, pre-internship professional commitment and stress perceived during internship predicted 40.1% of the variance in the post-internship commitment. Discussion These data summarize the nursing students’ professional commitment changes and the main influential factors that contribute to post-internship professional commitment of undergraduate nursing student. The findings are timely, which indicate that senior nursing students’ professional commitment can be increased by enhancing pre-internship commitment and reducing students’ stress levels during internship.

Polycystic ovary syndrome (PCOS) is the most common endocrine metabolic disorder among women of childbearing age, and self-management of PCOS patients relies on their ability to obtain health information. The proliferation of digital technologies, particularly social media and health applications, has fundamentally transformed health information-seeking behaviors (HISB) in this population. However, the present information behavior patterns of PCOS patients have not yet been systematically integrated. This scoping review aims to systematically map the landscape of HISB in women with PCOS by utilizing Wilson's model of information-seeking behavior as theoretical framework. It seeks to synthesize evidence on their information needs, preferred channels, behavioral types, and key influencing factors. The scoping review will adhere to Arksey and O'Malley's methodological framework and report following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review will include English-language literature published from inception up to November 30, 2025, searched through PubMed, Web of Science, Embase (Ovid), CINAHL, Cochrane Library, and APA PsycINFO. To find more relevant studies, we will also search grey literature, the reference lists of the included studies, and related systematic reviews. Two researchers will independently screen titles/abstracts, followed by full-text articles, to assess whether articles meet the inclusion criteria. A third researcher will resolve any discrepancies. Data extraction and narrative synthesis will be structured around the core constructs of Wilson's model, providing a theory-informed analysis of the evidence. Since this review involves collecting data from existing literature and does not involve human participants, ethical approval is not required. This scoping review will be submitted for publication to a peer reviewed academic journal.

Time-restricted eating (TRE) manages weight effectively, but choosing how long and what time window remain debatable. Although an 8:00 a.m. to 16:00 p.m. time frame is reported to show positive results in most weight loss trial, its safety and efficacy in overweight and obese women with polycystic ovary syndrome (PCOS) is uncertain. This randomized controlled trial is conducted to evaluate the safety and efficacy of TRE in specific populations. This study aims to assess the 6-month effects of TRE on weight change, metabolic improvement, reproductive recovery, and health-related quality of life in overweight and obese women with polycystic ovary syndrome (PCOS), compared to those who did not receive TRE. This randomized controlled trial will enroll 96 overweight and obese women with polycystic ovary syndrome (PCOS), who will be randomly assigned to either a TRE group (with an eating window from 8:00 a.m. to 16:00 p.m.) or a control group (without eating time restrictions), with 49 participants in each group. Evaluators and data analysts will remain blinded to group allocation throughout the study. The primary outcomes, including changes in weight and body mass index (BMI), will be assessed weekly. Secondary outcomes, encompassing alterations in sex hormones, metabolic parameters, body composition, sleep quality, quality of life, anxiety, and depression, will be evaluated monthly. Compliance and safety will be continuously monitored throughout the study. Additionally, a 6-month follow-up will be conducted at the end of the trial to assess the long-term effects of TRE. Statistical analysis will include the Anderson-Darling test for normality, T-test/Wilcoxon test based on distribution, mixed-effects models for assessing time/group effects, Cox model for time-to-event analysis, repeated ANOVA for change analysis, and sensitivity analysis. All tests will be conducted using appropriate software, with a significance level set at P<0.05. Missing data will be imputed. The purpose of this study protocol is to further evaluate the effects of TRE in overweight and obese women with PCOS through a randomized controlled trial (RCT). Findings from this study are expected to provide new dietary intervention strategies for overweight and obese PCOS participants. This study has received ethics approval from the Medical Ethics Committee of the University of South China (Number: NHHL027). Participants are included after signing informed consent. Results will be submitted for publication in peer-reviewed journals. Trail registration number: ChiCTR2400086815.

Objective This systematic review aims to synthesize the current evidence and develop evidence-based recommendations regarding time-restricted eating (TRE) for weight management in adults with overweight and obesity, addressing a gap in specific clinical guidelines. Methods We conducted a systematic search of nine databases and six websites for relevant literature up to September 2024. Included studies comprised randomized controlled trials (RCTs), clinical guidelines, expert consensus statements, and systematic reviews focusing on TRE in the target population. Two reviewers independently performed study selection, data extraction, and methodological quality assessment using standardized tools (e.g., AMSTAR 2, AGREE II, JBI checklists). Evidence was synthesized thematically, and recommendations were graded using the JBI framework. Results The search identified 5535 records. After screening, 25 articles were included: five guidelines, three expert consensuses, eight systematic reviews, and nine RCTs. The synthesis yielded 39 key evidence points across six domains: applicable populations, intervention protocols, dietary considerations, psychological and sleep effects, efficacy, and safety. The synthesized evidence suggests that TRE can induce significant weight loss and improve cardiometabolic parameters (e.g., blood glucose and lipid profiles) in the short to medium term. While heterogeneity exists across individual studies, this review identifies key factors (e.g., eating window protocols, adherence) that may influence outcomes and provides a framework for clinical decision-making. Conclusions TRE represents a promising dietary intervention for adults with overweight and obesity. This review provides a structured evidence summary and practical recommendations to guide its clinical application. Future research should focus on the long-term efficacy, sustainability, and impact of TRE on hard clinical endpoints. Level of evidence Level I, systematic review.

IntroductionWomen with gestational diabetes mellitus (GDM) are more likely to give birth to large for gestational age (LGA) infants, due to abnormalities in glucose metabolism during pregnancy. Although previous studies have explored the risk factors for LGA delivery in GDM women, the results are quite different and still lack of unified understanding.ObjectiveTo explore the elements linked to LGA delivery in GDM women, and thus provide a reference for medical staff to formulate relevant clinical interventions.Methods and analysisSystematic search of seven electronic databases (PubMed, Scopus, Cochrane Library, Web of Science, EMBASE, OVID and CINAHL) will be undertaken between the inception of the database to 1 August 2024. Quantitative studies published in English and focused on the risk factors for LGA delivery in GDM women will be included. Two researchers will independently screen the literature and any disagreements will be resolved by a third-party researcher. Joanna Briggs’s Institutional Critical Appraisal Tools will be used for the quality assessment of included studies. RevMan V.5.4 software will be used for data processing and summarising. To ensure the reliability and stability of the results, Q test and I2 test will be used to identify the heterogeneity between studies, while subgroup analysis and sensitivity analysis will be performed based on study quality.Ethics and disseminationThis systematic review and meta-analysis will be based on published literature, and the findings will be published in a peer-reviewed journal and presented at major conferences focused on clinical nursing.PROSPERO registration numberCRD42024559013.

The relationship between induced abortion(IA) and hypertensive disorders of pregnancy(HDP) is inconclusive. Few studies have been conducted in China. In order to clarify the association between previous IA and risk of HDP, including gestational hypertension(GH) and pre-eclampsia(PE), we performed a community-based prospective cohort study enrolling 5191 eligible nulliparous women in selected 2 districts and 11 towns of Liuyang from 2013 to 2015. Multivariable logistic regression was conducted to examine whether IA was associated with HDP, GH and PE. Of the gravidea, 1378(26.5%) had a previous IA and 258(5.0%) diagnosed with HDP, including 141(2.7%) GH and 117(2.3%) PE. The difference in the incidence of GH and PE between gravidae having one versus those with two or more IAs was minimal. After adjustment for maternal age, body mass index at first antenatal visit, education, virus infection and history of medical disorders, previous IA was significantly associated with HDP (OR = 0.67, 95%CI = 0.49 to 0.91) and PE (OR = 0.61, 95%CI = 0.38 to 0.97), but not with GH (OR = 0.73, 95%CI = 0.49 to 1.10). Additional adjustment for occupation, living area, anemia, gestational diabetes mellitus, psychological stress, conception climate and infant sex, multivariable analysis provided similar results. In conclusion, previous IA was associated with a lower risk of PE among nulliparous women.

Background Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication that can pose numerous adverse health effects on both mothers and newborns. Accurate prediction of the risk of GDM serves as a valuable supplement to prenatal education and clinical decision-making. Compared with traditional prediction models, artificial intelligence (AI) algorithms have demonstrated higher predictive accuracy and stronger individualization capabilities. However, the application of AI models in GDM prediction is still in a developmental stage, and their performance and clinical utility have not been thoroughly evaluated. Therefore, this study aims to systematically review and critically appraise the published predictive performance of AI models for GDM prediction and to offer insights for future research and practical application. Methods A systematic literature search will be performed across six databases (PubMed, Web of Science, Cochrane Library, Scopus, EMBASE, and OVID). Screening of titles and abstracts, full-text review, and data extraction will be independently completed by two authors. Qualitative data on the characteristics of the included studies, methodological quality, and the applicability of models will be summarized through narrative descriptions and tabulated formats. For models with predictive performance data from multiple studies, a random-effects meta-analysis or meta-regression will be employed to synthesize the findings, considering potential heterogeneity. Ethics and dissemination Ethical approval is deemed not applicable for this systematic review and meta-analysis. The findings will be based on published literature, disseminated through publication in a peer-reviewed journal, and presented at major conferences focused on clinical healthcare. Systematic review registration PROSPERO registration number CRD42025645913