METHODS: This systematic review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The literature search was carried out through PubMed, Web of Science, and Scopus. Original articles written in English and published between 2013 and 2023 were considered. Study quality was appraised using the Mixed Methods Appraisal Tool. Narrative synthesis was undertaken due to methodological heterogeneity in the included studies.
RESULTS: A total of 13 cross-sectional studies, two randomized controlled trials, two cohort studies, two mixed methods studies and one quasi-experiment with a control group were included. An overall low level of diabetes risk perception was reported particularly in those without apparent risk for diabetes. The 20 included studies reported widely varied measures for calculating diabetes risk perception. The influence of environmental factors on the risk perception of diabetes was highlighted.
LIMITATIONS: The use of study-specific and non-validated measures in the included studies weakens the authors' ability to compare across studies. The role of language and publication bias within this systematic review should be acknowledged as we included only English-language studies published in peer-reviewed journals. Another limitation is the exclusion of dimensions of risk perception such as optimistic bias as search terms.
CONCLUSION: The overall low risk perception of diabetes calls for urgent need of public health interventions to increase the risk perception of diabetes. In the future, researchers should ensure the validity and reliability of the measures being used. The influence of environmental factors on the diabetes risk perception indicates that diabetes preventive interventions targeting environmental factors may be effective in increasing the risk perception of diabetes.
RECENT FINDINGS: Evidence from previous studies highlights the association between digital addiction and loneliness among adolescents. Overusing digital devices among adolescents is also associated with various physical and psychological side effects.
SUMMARY: Recent findings support the rapid rise of digital device usage among adolescents and its contributions to digital use. More research is needed to support existing interventions, provide early screening, and combat digital addiction to protect adolescents from the risks of loneliness due to the overuse of digital devices.
METHODS: We source daily death registry data for 4100 municipalities in Italy's north and match them to Census data. We augment the dataset with municipality-level data on a host of co-factors of COVID-19 mortality, which we exploit in a differences-in-differences regression model to analyze COVID-19-induced mortality.
RESULTS: We find that COVID-19 killed more than 0.15% of the local population during the first wave of the epidemic. We also show that official statistics vastly underreport this death toll, by about 60%. Next, we uncover the dramatic effects of the epidemic on nursing home residents in the outbreak epicenter: in municipalities with a high share of the elderly living in nursing homes, COVID-19 mortality was about twice as high as in those with no nursing home intown.
CONCLUSIONS: A pro-active approach in managing the epidemic is key to reduce COVID-19 mortality. Authorities should ramp-up testing capacity and increase contact-tracing abilities. Adequate protective equipment should be provided to nursing home residents and staff.
METHODS AND FINDINGS: We searched the major electronic databases Medline, Embase, and Google Scholar (January 1990-October 2018) without language restrictions. We included cohort studies on term pregnancies that provided estimates of stillbirths or neonatal deaths by gestation week. We estimated the additional weekly risk of stillbirth in term pregnancies that continued versus delivered at various gestational ages. We compared week-specific neonatal mortality rates by gestational age at delivery. We used mixed-effects logistic regression models with random intercepts, and computed risk ratios (RRs), odds ratios (ORs), and 95% confidence intervals (CIs). Thirteen studies (15 million pregnancies, 17,830 stillbirths) were included. All studies were from high-income countries. Four studies provided the risks of stillbirth in mothers of White and Black race, 2 in mothers of White and Asian race, 5 in mothers of White race only, and 2 in mothers of Black race only. The prospective risk of stillbirth increased with gestational age from 0.11 per 1,000 pregnancies at 37 weeks (95% CI 0.07 to 0.15) to 3.18 per 1,000 at 42 weeks (95% CI 1.84 to 4.35). Neonatal mortality increased when pregnancies continued beyond 41 weeks; the risk increased significantly for deliveries at 42 versus 41 weeks gestation (RR 1.87, 95% CI 1.07 to 2.86, p = 0.012). One additional stillbirth occurred for every 1,449 (95% CI 1,237 to 1,747) pregnancies that advanced from 40 to 41 weeks. Limitations include variations in the definition of low-risk pregnancy, the wide time span of the studies, the use of registry-based data, and potential confounders affecting the outcome.
CONCLUSIONS: Our findings suggest there is a significant additional risk of stillbirth, with no corresponding reduction in neonatal mortality, when term pregnancies continue to 41 weeks compared to delivery at 40 weeks.
SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42015013785.
OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace.
RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk.
CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.