The effectiveness of interventions on improving body image for pregnant and postpartum women: a systematic review of randomized clinical trials.

BACKGROUND: Body image dissatisfaction, leading to a variety of negative emotions and adverse pregnancy or birth outcomes. Studies on body image interventions for pregnant and postpartum women have been reported, yielding mixed results. Existing evidence lacks a comprehensive review of the effectiveness of body image interventions for pregnant and postpartum women. OBJECTIVE: The aim of this study was to systematically review interventions which aimed at improving body image during pregnancy and postpartum in women of childbearing age, and further to explore their effectiveness. METHODS: A comprehensive literature search was conducted using electronic databases, including PubMed, Embase, Web of Science, Cochrane Library, CINAHL, SinoMed, CNKI, and Wanfang Database, to retrieve relevant studies. Body image was reported employing descriptive analysis, whereas the Cochrane Handbook tool was used to evaluate the quality and potential bias of each included study. RESULTS: Following established inclusion and exclusion criteria, 11 studies were identified from an initial 1,422 records for further analysis, involving 1290 participants. This systematic review grouped body image interventions into lifestyle interventions and psychological interventions based on their content. These interventions yielded more pronounced positive effects on improving body image in pregnant and postpartum women when compared to control groups. And, the statistical difference on psychological interventions is more significant on the whole. CONCLUSIONS: Our work offers a comprehensive overview of the effectiveness of body image interventions for pregnant and postpartum women. Psychological interventions are considered to be a suitable measure to improve body image for pregnant or postpartum women. Additional research and practical applications are recommended to enhance the mental health and well-being of perinatal women. TRIAL REGISTRATION: PROSPERO registry: CRD42024531531.

The effectiveness of digital twins in promoting precision health across the entire population: a systematic review.

Digital twins represent a promising technology within the domain of precision healthcare, offering significant prospects for individualized medical interventions. Existing systematic reviews, however, mainly focus on the technological dimensions of digital twins, with a limited exploration of their impact on health-related outcomes. Therefore, this systematic review aims to explore the efficacy of digital twins in improving precision healthcare at the population level. The literature search for this study encompassed PubMed, Embase, Web of Science, Cochrane Library, CINAHL, SinoMed, CNKI, and Wanfang Database to retrieve potentially relevant records. Patient health-related outcomes were synthesized employing quantitative content analysis, whereas the Joanna Briggs Institute (JBI) scales were used to evaluate the quality and potential bias inherent in each selected study. Following established inclusion and exclusion criteria, 12 studies were screened from an initial 1321 records for further analysis. These studies included patients with various conditions, including cancers, type 2 diabetes, multiple sclerosis, heart failure, qi deficiency, post-hepatectomy liver failure, and dental issues. The review coded three types of interventions: personalized health management, precision individual therapy effects, and predicting individual risk, leading to a total of 45 outcomes being measured. The collective effectiveness of these outcomes at the population level was calculated at 80% (36 out of 45). No studies exhibited unacceptable differences in quality. Overall, employing digital twins in precision health demonstrates practical advantages, warranting its expanded use to facilitate the transition from the development phase to broad application.PROSPERO registry: CRD42024507256.

Clinical First-Trimester Prediction Models for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common pregnancy complication that negatively impacts the health of both the mother and child. Early prediction of the risk of GDM may permit prompt and effective interventions. This systematic review and meta-analysis aimed to summarize the study characteristics, methodological quality, and model performance of first-trimester prediction model studies for GDM. METHODS: Five electronic databases, one clinical trial register, and gray literature were searched from the inception date to March 19, 2022. Studies developing or validating a first-trimester prediction model for GDM were included. Two reviewers independently extracted data according to an established checklist and assessed the risk of bias by the Prediction Model Risk of Bias Assessment Tool (PROBAST). We used a random-effects model to perform a quantitative meta-analysis of the predictive power of models that were externally validated at least three times. RESULTS: We identified 43 model development studies, six model development and external validation studies, and five external validation-only studies. Body mass index, maternal age, and fasting plasma glucose were the most commonly included predictors across all models. Multiple estimates of performance measures were available for eight of the models. Summary estimates range from 0.68 to 0.78 (I2 ranged from 0% to 97%). CONCLUSION: Most studies were assessed as having a high overall risk of bias. Only eight prediction models for GDM have been externally validated at least three times. Future research needs to focus on updating and externally validating existing models.