Introduction This is a 12-weeks randomized controlled trial examining the effects of aerobic exercise (AE), computerized cognitive training ( CCT) and their combination (COMB). We aim to investigate their impact on cardiovascular health and white matter (WM) integrity and how they contribute to the cognitive benefits.
Methods 109 participants were recruited and 82 (62% female; age = 58.38 +/- 5.47) finished the intervention with > 80% adherence. We report changes in cardiovascular risk factors and WM integrity (fractional anisotropy (FA); mean diffusivity (MD)), how they might be related to changes in physical activity, age and sex, and their potential role as mediators in cognitive improvements.
Results A decrease in BMI (SMD = – 0.32, p = 0.039), waist circumference (SMD = – 0.42, p = 0.003) and diastolic blood pressure (DBP) (SMD = – 0.42, p = 0.006) in the AE group and a decrease in BMI (SMD = – 0.34, p = 0.031) and DBP (SMD = – 0.32, p = 0.034) in the COMB group compared to the waitlist control group was observed. We also found decreased global MD in the CCT group (SMD = – 0.34; p = 0.032) and significant intervention-related changes in FA and MD in the frontal and temporal lobes in the COMB group.
Conclusions We found changes in anthropometric measures that suggest initial benefits on cardiovascular health after only 12 weeks of AE and changes in WM microstructure in the CCT and COMB groups. These results add evidence of the clinical relevance of lifestyle interventions and the potential benefits when combining them.

BACKGROUND: The effectiveness of the immunity provided by SARS-CoV-2 vaccines is an important public health issue. We analyzed the determinants of 12-month serology in a multicenter European cohort of vaccinated healthcare workers (HCW). METHODS: We analyzed the sociodemographic characteristics and levels of anti-SARS-CoV-2 spike antibodies (IgG) in a cohort of 16,101 vaccinated HCW from eleven centers in Germany, Italy, Romania, Slovakia and Spain. Considering the skewness of the distribution, the serological levels were transformed using log or cubic standardization and normalized by dividing them by center-specific standard errors. We fitted center-specific multivariate regression models to estimate the cohort-specific relative risks (RR) of an increase of one standard deviation of log or cubic antibody level and the corresponding 95% confidence interval (CI) for different factors and combined them in random-effects meta-analyses. RESULTS: We included 16,101 HCW in the analysis. A high antibody level was positively associated with age (RR = 1.04, 95% CI = 1.00-1.08 per 10-year increase), previous infection (RR = 1.78, 95% CI 1.29-2.45) and use of Spikevax [Moderna] with combinations compared to Comirnaty [BioNTech/Pfizer] (RR = 1.07, 95% CI 0.97-1.19) and was negatively associated with the time since last vaccine (RR = 0.94, 95% CI 0.91-0.98 per 30-day increase). CONCLUSIONS: These results provide insight about vaccine-induced immunity to SARS-CoV-2, an analysis of its determinants and quantification of the antibody decay trend with time since vaccination.

Background Missed nursing care is defined as care that is delayed, partially completed, or not completed at all. The scenario created by the COVID-19 pandemic may have influenced multifactorial determinants related to the care environment, nursing processes, internal processes, and decision-making processes, increasing missed nursing care.AimThis scoping review aimed to establish the quantity and type of research undertaken on missed nursing care during the COVID-19 pandemic.Methods This review was conducted following the Joanna Briggs Institute methodology for scoping reviews. We searched CINAHL, MEDLINE, Scopus, two national and regional databases, two dissertations and theses databases, a gray literature database, two study registers, and a search engine from November 1, 2019, to March 23, 2023. We included quantitative, qualitative, and mixed studies carried out in all healthcare settings that examined missed nursing care during the COVID-19 pandemic. Language restrictions were not applied. Two independent reviewers conducted study selection and data extraction. Disagreements between the reviewers were resolved through discussion or with an additional reviewer.Results We included 25 studies with different designs, the most common being acute care cross-sectional survey designs. Studies focused on determining the frequency and reasons for missed nursing care and its influence on nurses and organizational outcomes.Linking Evidence to Action Missed nursing care studies during the COVID-19 pandemic were essentially nurses-based prevalence surveys. There is an urgent need to advance the design and development of longitudinal and intervention studies, as well as to broaden the focus of research beyond acute care. Further research is needed to determine the impact of missed nursing care on nursing-sensitive outcomes and from the patient’s perspective.

BACKGROUND: Liver cirrhosis is a major cause of death worldwide. Cirrhosis develops after a long asymptomatic period of fibrosis progression, with the diagnosis frequently occurring late, when major complications or cancer develop. Few reliable tools exist for timely identification of individuals at risk of cirrhosis to allow for early intervention. We aimed to develop a novel score to identify individuals at risk for future liver-related outcomes. METHODS: We derived the LiverRisk score from an international prospective cohort of individuals from six countries without known liver disease from the general population, who underwent liver fibrosis assessment by transient elastography. The score included age, sex, and six standard laboratory variables. We created four groups: minimal risk, low risk, medium risk, and high risk according to selected cutoff values of the LiverRisk score (6, 10, and 15). The model’s discriminatory accuracy and calibration were externally validated in two prospective cohorts from the general population. Moreover, we ascertained the prognostic value of the score in the prediction of liver-related outcomes in participants without known liver disease with median follow-up of 12 years (UK Biobank cohort). FINDINGS: We included 14 726 participants: 6357 (43·2%) in the derivation cohort, 4370 (29·7%) in the first external validation cohort, and 3999 (27·2%) in the second external validation cohort. The score accurately predicted liver stiffness in the development and external validation cohorts, and was superior to conventional serum biomarkers of fibrosis, as measured by area under the receiver-operating characteristics curve (AUC; 0·83 [95% CI [0·78-0·89]) versus the fibrosis-4 index (FIB-4; 0·68 [0·61-0·75] at 10 kPa). The score was effective in identifying individuals at risk of liver-related mortality, liver-related hospitalisation, and liver cancer, thereby allowing stratification to different risk groups for liver-related outcomes. The hazard ratio for liver-related mortality in the high-risk group was 471 (95% CI 347-641) compared with the minimal risk group, and the overall AUC of the score in predicting 10-year liver-related mortality was 0·90 (0·88-0·91) versus 0.84 (0·82-0·86) for FIB-4. INTERPRETATION: The LiverRisk score, based on simple parameters, predicted liver fibrosis and future development of liver-related outcomes in the general population. The score might allow for stratification of individuals according to liver risk and thus guide preventive care. FUNDING: European Commission under the H20/20 programme; Fondo de Investigación Sanitaria de Salud; Instituto de Salud Carlos III; Spanish Ministry of Economy, Industry, and Competitiveness; the European Regional Development Fund; and the German Ministry of Education and Research (BMBF).

Objective To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict health-related outcomes.Methods This review included studies in any language that: EHR was at least one of the data sources, collected longitudinal data, used an AI technique capable of handling longitudinal data, and predicted any health-related outcomes. We searched MEDLINE, Scopus, Web of Science, and IEEE Xplorer from inception to January 3, 2022. Information on the dataset, prediction task, data preprocessing, feature selection, method, validation, performance, and implementation was extracted and summarized using descriptive statistics. Risk of bias and completeness of reporting were assessed using a short form of PROBAST and TRIPOD, respectively.Results Eighty-one studies were included. Follow-up time and number of registers per patient varied greatly, and most predicted disease development or next event based on diagnoses and drug treatments. Architectures generally were based on Recurrent Neural Networks-like layers, though in recent years combining different layers or transformers has become more popular. About half of the included studies performed hyperparameter tuning and used attention mechanisms. Most performed a single train-test partition and could not correctly assess the variability of the model’s performance. Reporting quality was poor, and a third of the studies were at high risk of bias.Conclusions AI models are increasingly using longitudinal data. However, the heterogeneity in reporting methodology and results, and the lack of public EHR datasets and code sharing, complicate the possibility of replication.Registration PROSPERO database (CRD42022331388).