Background: The increasing incidence of gestational diabetes mellitus (GDM) is a global health problem that is more likely to occur in pregnant women with overweight or obesity. Adhering to a healthy lifestyle is associated with a reduced risk of GDM. With the development of IT, mobile health (mHealth) interventions have become widely available in health care. However, there are no definitive conclusions on the effectiveness of mHealth-based lifestyle interventions in preventing GDM. Objective: This study aims to evaluate the impact of mHealth-based lifestyle interventions on GDM and other pregnancy outcomes in pregnant women with overweight or obesity. Methods: A systematic literature search was conducted in 5 English databases (MEDLINE, Embase, Web of Science, CENTRAL, and CINAHL) and 4 Chinese databases (CBM, CNKI, Vip, and Wanfang) to identify randomized controlled trials (RCTs) on the effectiveness of mHealth-based interventions for GDM from inception to January 10, 2023. In total, 2 authors independently screened the studies and extracted the data. The quality of the included studies was examined using the Cochrane risk-of-bias tool. Data synthesis was conducted using Review Manager (version 5.4; The Cochrane Collaboration). Results: A total of 16 RCTs with 7351 participants were included in this study. The included studies were published between 2014 and 2021 and were conducted in China, the United States, Australia, New Zealand, the United Kingdom, Ireland, and Norway. The sample sizes of the studies ranged from 75 to 2202, and the duration of the mHealth-based lifestyle interventions ranged from 4 to 28 weeks. Compared with usual care, mHealth-based lifestyle interventions significantly reduced the incidence of GDM (odds ratio [OR] 0.74, 95% CI 0.56-0.96; P=.03; I2=65%), preterm birth (OR 0.65, 95% CI 0.48-0.87; P=.004; I2=25%), macrosomia (OR 0.59, 95% CI 0.40-0.87; P=.008; I2=59%), and gestational weight gain (mean difference=−1.12 kg, 95% CI −1.44 to −0.80; P<.001; I2=43%). The subgroup analysis showed that interventions delivered via apps (OR 0.55, 95% CI 0.37-0.83; P=.004; I2=44%), provided by obstetricians (OR 0.69, 95% CI 0.51-0.93; P=.02; I2=60%), and targeted at Asian populations (OR 0.44, 95% CI 0.34-0.58; P<.001; I2=0%) and that used the International Association of Diabetes and Pregnancy Study Groups diagnostic criteria (OR 0.58, 95% CI 0.39-0.86; P=.007; I2=69%) showed a statistically significant reduction in the risk of GDM. Conclusions: mHealth-based lifestyle interventions had a favorable impact on the prevention of GDM in pregnant women with overweight and obesity. Future studies need to further explore the potential of mHealth-based interventions for GDM through better design and more rigorous large-scale RCTs. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42021286995; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=286995

COVID-19-related lockdown measures have been affecting children’s weight status and weight-related behaviors, which are often associated with an increase in childhood obesity. However, large-scale longitudinal studies are lacking. Our study aimed to analyze changes in obesity and weight-related behaviors in Chinese children before and during the COVID-19 pandemic and provide references for addressing the high prevalence of childhood obesity. A prospective multi-center longitudinal survey was conducted among Chinese children (n = 5963), collecting data on weight status, COVID-19-related measures, and lifestyle behaviors. Changes were assessed using t-tests and χ² tests for paired samples, or the Wilcoxon signed-rank test, according to the type and distribution of data. The Generalized Estimating Equations model was used to explore influential factors of obesity. The prevalence of overweight and obesity increased from 9.2% and 8.6% before the pandemic to 10.5% and 10.6% during the pandemic (p < 0.001), respectively. Daily physical activity, sleep duration, and sugar-sweetened beverage consumption decreased while screen time increased. The results of the generalized estimating equations showed that ethnic minority, older age, less daily physical activity, reduced sleep duration, and longer screen time were positively associated with obesity. There is an intensifying trend of obesity in children in the context of the COVID-19 pandemic, to which altered weight-related behaviors might have contributed largely. Maintaining a healthy lifestyle, especially in social crises, should be highlighted to ease the burden of childhood obesity.

To determine the effectiveness and dose–response of nurse-led hospital-to-home transitional care interventions (TCIs) on patient mortality and psychosocial outcomes of health-related quality of life (HRQoL), self-care behaviours, and emotional well-being in adults hospitalized with heart failure (HF) and to recognize pertinent characteristics that potentially affect the overall effectiveness. Relevant studies were identified through electronic database searches, including MEDLINE, Embase, CINAHL, and Cochrane Library from January 2000 until January 2021. Two independent authors performed study selection, data abstraction, and risk-of-bias assessment. When appropriate, we used random-effects meta-analysis to derive pooled effect estimates, investigated dose–response relationships, and ran meta-regressions to locate the source of heterogeneity. A total of 27 studies with 7635 participants were included. Our findings revealed that nurse-led hospital-to-home TCIs reduced the risk of all-cause mortality by 21% [risk ratio = 0.79; 95% confidence interval (CI) 0.68–0.92; P = 0.003] and improved HRQoL (mean difference = −3.29; 95% CI −6.51 to −0.07; P = 0.04) compared to usual care, but non-significant effects were found for emotional well-being. The narrative summary of evidence for self-care behaviours showed positive intervention effects. Meta-regression did not find any covariates that were significantly related to mortality or HRQoL. Dose–response analysis showed that mortality risk was reduced with increased intensity and complexity of the nurse-led TCIs. Generally, nurse-led hospital-to-home TCIs may play a beneficial role in decreasing mortality, and improving HRQoL and self-care behaviours for adults with HF. Additional studies are warranted to characterize the optimal nurse-led TCIs for HF management.

Background: Internet addiction and depression frequently co‐occur among university students, resulting in amplified functional deterioration and treatment resistance. Despite established bidirectional relationships, existing research has predominantly examined linear associations and treated these conditions as single global constructs. This study integrated person‐centered and network‐based approaches to identify distinct symptom profiles of Internet addiction and depressive symptoms, examine sociodemographic predictors of profile membership, and uncover interconnected symptom networks within high‐risk populations among Chinese university students. Methods: A multicenter cross‐sectional study was conducted from April to July 2024. Data were collected through a web‐based survey incorporating validated instruments for Internet addiction, depression, and suicide risk assessment. Latent profile analysis was employed to identify distinct symptom profiles, followed by multivariate logistic regression to examine sociodemographic predictors. Network analysis was performed within the high‐risk profile to unveil symptom interactions, central symptoms, bridge symptoms, and symptomatic pathways to suicide risk. Results: Among 30,992 participants, latent profile analysis identified three distinct groups: Healthy profile (59.31%), at‐risk profile (35.06%), and comorbidity profile (5.63%). Students who were female, ethnic minorities, in higher grade levels, and had prolonged Internet use showed increased risks of problematic profiles. Conversely, enrollment in bachelor’s programs, science and medical majors, higher household income, and regular physical activity demonstrated protective effects. Network analysis revealed Internet preoccupation and fatigue as central symptoms, identified key bridge symptoms (e.g., offline negative affect, difficulty concentrating) linking the symptom clusters, and highlighted Internet withdrawal symptoms and depressed mood as critical pathways to suicide risk within the comorbidity profile. Conclusion: This study identified distinct profiles of Internet addiction and depression comorbidity, with specific sociodemographic and lifestyle predictors informing targeted screening strategies. Network analysis revealed central symptoms and specific bridge symptoms connecting the conditions, while also identifying critical pathways to suicide risk in the Comorbidity profile, providing empirical evidence for developing precise and effective interventions.

(1) Background: Female body composition undergoes significant changes to support fetal growth and development during pregnancy. This study investigated the association of maternal body composition in the second trimester and macrosomia and explored whether body-composition-related indicators could be used to predict macrosomia. (2) Methods: This study was conducted in China from December 2016 to December 2021. Women with singleton pregnancies, gestational ages between 37 and 42 weeks, and an absence of pregnancy complications were included. In the second trimester, bioelectric impedance analysis (BIA) was used to measure body-composition-related indicators. Logistic regression analysis was performed to explore the risk factors for macrosomia. The predictive performance of maternal body composition and clinical indicators for macrosomia were assessed using the area under the receiver-operating-characteristics curve (AUC). (3) Results: This retrospective study involved 43,020 pregnant women; we collected 2008 cases of macrosomia. Gravidity, gestational age, body mass index (BMI), gestational weight gain (GWG), total body water, fat mass, fat-free mass (FFM), skeletal muscle mass, and visceral fat level were risk factors for macrosomia (p < 0.05 for all). In the prediction model, the AUC of FFM for predicting macrosomia was the largest (0.742). (4) Conclusions: Body-composition-related indicators associated with macrosomia and body composition measurements in the second trimester can predict the risk of macrosomia, enabling clinicians to implement interventions earlier to reduce adverse perinatal outcomes.