Aims: Fear of physical activity (PA) is discussed as a barrier to regular exercise in patients with heart failure (HF), but HF-specific theoretical concepts are lacking. This study examined associations of fear of PA, heart-focused anxiety and trait anxiety with clinical characteristics and self-reported PA in outpatients with chronic HF. It was also investigated whether personality-related coping styles for dealing with health threats impact fear of PA via symptom perception. Methods and results: This cross-sectional study enrolled 185 HF outpatients from five hospitals (mean age 62 ± 11 years, mean ejection fraction 36.0 ± 12%, 24% women). Avoidance of PA, sports/exercise participation (yes/no) and the psychological characteristics were assessed by self-reports. Fear of PA was assessed by the Fear of Activity in Situations–Heart Failure (FActS-HF15) questionnaire. In multivariable regression analyses higher NYHA class (b = 0.26, p = 0.036) and a higher number of HF drugs including antidepressants (b = 0.25, p = 0.017) were independently associated with higher fear of PA, but not with heart-focused fear and trait anxiety. Of the three anxiety scores only increased fear of PA was independently associated with more avoidance behavior regarding PA (b = 0.45, SE = 0.06, p < 0.001) and with increased odds of no sports/exercise participation (OR = 1.34, 95% CI 1.03–1.74, p = 0.028). Attention towards cardiac symptoms and symptom distress were positively associated with fear of PA (p < 0.001), which explained higher fear of PA in patients with a vigilant (directing attention towards health threats) coping style (p = 0.004). Conclusions: Fear of PA assessed by the FActS-HF15 is a specific type of anxiety in patients with HF. Attention towards and being distressed by HF symptoms appear to play a central role in fear of PA, particularly in vigilant patients who are used to direct their attention towards health threats. These findings provide approaches for tailored interventions to reduce fear of PA and to increase PA in patients with HF.

Au Luxembourg, des médiateur·trice·s scolaires externes apportent leur aide quand des conflits surgissent en milieu scolaire. Le Service de médiation scolaire fournit un soutien en cas de risque de décrochage scolaire et de conflits en lien avec l’inclusion et l’intégration d’élèves à besoins éducatifs spécifiques ou issu·e·s de l’immigration. Michèle Schilt s’est entretenue avec la directrice du service, Lis De Pina, sur le travail des médiateur·trice·s scolaires.

Investment theory and related theoretical approaches suggest a dynamic interplay between crystallized intelligence, fluid intelligence, and investment traits like need for cognition. Although cross-sectional studies have found positive correlations between these constructs, longitudinal research testing all of their relations over time is scarce. In our pre-registered longitudinal study, we examined whether initial levels of crystallized intelligence, fluid intelligence, and need for cognition predicted changes in each other. We analyzed data from 341 German students in grades 7–9 who were assessed twice, one year apart. Using multi-process latent change score models, we found that changes in fluid intelligence were positively predicted by prior need for cognition, and changes in need for cognition were positively predicted by prior fluid intelligence. Changes in crystallized intelligence were not significantly predicted by prior Gf, prior NFC, or their interaction, contrary to theoretical assumptions. This pattern of results was largely replicated in a model including all constructs simultaneously. Our findings support the notion that intelligence and investment traits, particularly need for cognition, positively interact during cognitive development, but this interplay was unexpectedly limited to Gf.

　

Data fusions are becoming increasingly relevant in official statistics. The aim of a data fusion is to combine two or more data sources using statistical methods in order to be able to analyse different characteristics that were not jointly observed in one data source. Record linkage of official data sources using unique identifiers is often not possible due to methodological and legal restrictions. Appropriate data fusion methods are therefore of central importance in order to use the diverse data sources of official statistics more effectively and to be able to jointly analyse different characteristics. However, the literature lacks comprehensive evaluations of which fusion approaches provide promising results for which data constellations. Therefore, the central aim of this thesis is to evaluate a concrete plethora of possible fusion algorithms, which includes classical imputation approaches as well as statistical and machine learning methods, in selected data constellations. To specify and identify these data contexts, data and imputation-related scenario types of a data fusion are introduced: Explicit scenarios, implicit scenarios and imputation scenarios. From these three scenario types, fusion scenarios that are particularly relevant for official statistics are selected as the basis for the simulations and evaluations. The explicit scenarios are the fulfilment or violation of the Conditional Independence Assumption (CIA) and varying sample sizes of the data to be matched. Both aspects are likely to have a direct, that is, explicit, effect on the performance of different fusion methods. The summed sample size of the data sources to be fused and the scale level of the variable to be imputed are considered as implicit scenarios. Both aspects suggest or exclude the applicability of certain fusion methods due to the nature of the data. The univariate or simultaneous, multivariate imputation solution and the imputation of artificially generated or previously observed values in the case of metric characteristics serve as imputation scenarios. With regard to the concrete plethora of possible fusion algorithms, three classical imputation approaches are considered: Distance Hot Deck (DHD), the Regression Model (RM) and Predictive Mean Matching (PMM). With Decision Trees (DT) and Random Forest (RF), two prominent tree-based methods from the field of statistical learning are discussed in the context of data fusion. However, such prediction methods aim to predict individual values as accurately as possible, which can clash with the primary objective of data fusion, namely the reproduction of joint distributions. In addition, DT and RF only comprise univariate imputation solutions and, in the case of metric variables, artificially generated values are imputed instead of real observed values. Therefore, Predictive Value Matching (PVM) is introduced as a new, statistical learning-based nearest neighbour method, which could overcome the distributional disadvantages of DT and RF, offers a univariate and multivariate imputation solution and, in addition, imputes real and previously observed values for metric characteristics. All prediction methods can form the basis of the new PVM approach. In this thesis, PVM based on Decision Trees (PVM-DT) and Random Forest (PVM-RF) is considered. The underlying fusion methods are investigated in comprehensive simulations and evaluations. The evaluation of the various data fusion techniques focusses on the selected fusion scenarios. The basis for this is formed by two concrete and current use cases of data fusion in official statistics, the fusion of EU-SILC and the Household Budget Survey on the one hand and of the Tax Statistics and the Microcensus on the other. Both use cases show significant differences with regard to different fusion scenarios and thus serve the purpose of covering a variety of data constellations. Simulation designs are developed from both use cases, whereby the explicit scenarios in particular are incorporated into the simulations. The results show that PVM-RF in particular is a promising and universal fusion approach under compliance with the CIA. This is because PVM-RF provides satisfactory results for both categorical and metric variables to be imputed and also offers a univariate and multivariate imputation solution, regardless of the scale level. PMM also represents an adequate fusion method, but only in relation to metric characteristics. The results also imply that the application of statistical learning methods is both an opportunity and a risk. In the case of CIA violation, potential correlation-related exaggeration effects of DT and RF, and in some cases also of RM, can be useful. In contrast, the other methods induce poor results if the CIA is violated. However, if the CIA is fulfilled, there is a risk that the prediction methods RM, DT and RF will overestimate correlations. The size ratios of the studies to be fused in turn have a rather minor influence on the performance of fusion methods. This is an important indication that the larger dataset does not necessarily have to serve as a donor study, as was previously the case. The results of the simulations and evaluations provide concrete implications as to which data fusion methods should be used and considered under the selected data and imputation constellations. Science in general and official statistics in particular benefit from these implications. This is because they provide important indications for future data fusion projects in order to assess which specific data fusion method could provide adequate results along the data constellations analysed in this thesis. Furthermore, with PVM this thesis offers a promising methodological innovation for future data fusions and for imputation problems in general.