Background: Digital twins (DTs) are digital representations of real-world systems, enabling advanced simulations, predictive modeling, and real-time optimization in various fields, including health care. Despite growing interest, the integration of DTs in health care faces challenges such as fragmented applications, ethical concerns, and barriers to adoption. Objective: This study systematically reviews the existing literature on DT applications in health care with three objectives: (1) to map primary applications, (2) to identify key challenges and limitations, and (3) to highlight gaps that can guide future research. Methods: A meta-review was conducted in a systematic fashion, adhering to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, and included 25 literature reviews published between 2021 and 2024. The search encompassed 5 databases: PubMed, CINAHL, Web of Science, Embase, and PsycINFO. Thematic synthesis was used to categorize DT applications, stakeholders, and barriers to adoption. Results: A total of 3 primary DT applications in health care were identified: personalized medicine, operational efficiency, and medical research. While current applications, such as predictive diagnostics, patient-specific treatment simulations, and hospital resource optimization, remain in their early stages of development, they highlight the significant potential of DTs. Challenges include data quality, ethical issues, and socioeconomic barriers. This review also identified gaps in scalability, interoperability, and clinical validation. Conclusions: DTs hold transformative potential in health care, providing individualized care, operational optimization, and accelerated research. However, their adoption is hindered by technical, ethical, and financial barriers. Addressing these issues requires interdisciplinary collaboration, standardized protocols, and inclusive implementation strategies to ensure equitable access and meaningful impact.

A better understanding of the contribution of the socioeconomic status (SES) in sleep health could guide the development of population-based interventions aiming to reduce “the silent public health issue” that are sleep disturbances. PRISMA was employed to identify relevant studies having examined the association between social class, social capital, education, income/assets, occupation/employment status, neighborhood deprivation and sleep health. Sixteen cross-sectional and three longitudinal studies were selected, having sampled 226,029 participants aged from 2 months to 85 years old. Findings showed that: (1) sleep health disparities among children and adolescent are strongly correlated to parental socioeconomic indicators; (2) poor parental income, poor family SES and poor parental education are associated with higher sleep disturbances among children and adolescents; (3) lower education is a predictor of increased sleep disturbances for adults; (4) low SES is associated with high sleep disturbances in adults and old people and; (5) low income and full-time employment was significantly associated with short sleep among adults and old people. In conclusion, sleep health should be an important public health target. Such intervention would be beneficial for populational health, for all taxpayers and public administrations, which would see a reduction in absenteeism and productivity losses attributable to sleep-related health problems in the global economy.

This review focuses on factors contributing to sleep quality among pregnant women with low socioeconomic statuses during the third trimester of their pregnancy. Electronic searches were conducted, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We searched for published, peer reviewed, English language primary research articles using electronic databases including PubMed, EMBASE, Ovid, MEDLINE and Google Scholar ending June 2019. All references were reviewed manually and independently by authors. After applying the inclusion criteria, 56 articles were selected; 38 of which are full-text and included in this review. All articles related to the analysis of poor sleep quality among uncomplicated pregnant women were included. Pregnant women with a specific pathology were excluded. We found poor sleep quality among pregnant women is correlated with low socioeconomic levels. Pregnant women with lower incomes tend to have inadequate diets, which further complicates the health of the mother and the baby. External factors including low income, poor quality of life and poor diet tend to increase the possibility of future health complications in both mother and child, and can result in complications such as preterm labor, low birth weight, preeclampsia, perinatal death, and spontaneous abortion.