Subjective well-being and exercise in the second halt of life: a critical review of theoretical approaches

Research has shown repeatedly that the “feeling better” effect of exercise is far more moderate than generally claimed. Examinations of subgroups in secondary analyses also indicate that numerous further variables influence this relationship. One reason for inconsistencies in this research field is the lack of adequate theoretical analyses. Well-being output variables frequently possess no construct definition, and little attention is paid to moderating and mediating variables. This article integrates the main models in an overview and analyzes how secondary analyses define well-being and which areas of the construct they focus on. It then applies a moderator and/or mediator framework to examine which person and environmental variables can be found in the existing explanatory approaches in sport science and how they specify the influence of these moderating and mediating variables. Results show that the broad understanding of well-being in many secondary analyses makes findings difficult to interpret. Moreover, physiological explanatory approaches focus more on affective changes in well-being, whereas psychological approaches also include cognitive changes. The approaches focus mostly on either physical or psychological person variables and rarely combine the two, as in, for example, the dual-mode model. Whereas environmental variables specifying the treatment more closely (e.g., its intensity) are comparatively frequent, only the social support model formulates variables such as the framework in which exercise is presented. The majority of explanatory approaches use simple moderator and/or mediator models such as the basic mediated (e.g., distraction hypothesis) or multiple mediated (e.g., monoamine hypotheses) model. The discussion draws conclusions for future research.

Subjective pubertal timing and health compromising behaviors among on- time Swiss adolescent girls

AIM: The aim of this research is to assess the associations between subjective pubertal timing (SPT) and onset of health-compromising behaviours among girls reporting an on-time objective pubertal timing (OPT). METHODS: Data were drawn from the Swiss SMASH 2002 survey, a self-administered questionnaire study conducted among a nationally representative sample of 7548 adolescents aged 16-20 years. From the 3658 girls in the initial sample, we selected only those (n = 1003) who provided information about SPT and who reported the average age at menarche, namely 13, considering this as an on-time OPT. Bivariate and logistic analyses were conducted to compare the early, on-time and late SPT groups in terms of onset of health-compromising behaviours. RESULTS: A perception of pubertal precocity was associated with sexual intercourse before age 16 [adjusted odds ratio (AOR): 2.10 (1.30-3.37)] and early use of illegal drugs other than cannabis [AOR: 2.55 (1.30-5.02)]. Conversely, girls perceiving their puberty as late were less likely to report intercourse before age 16 [AOR: 0.30 (0.12-0.75)]. CONCLUSION: Faced with an adolescent girl perceiving her puberty as early, the practitioner should investigate the existence of health-compromising behaviours even if her puberty is or was objectively on-time.

Social support and oxytocin interact to suppress cortisol and subjective responses to psychosocial stress

BACKGROUND The presence of social support has been associated with decreased stress responsiveness. Recent animal studies suggest that the neuropeptide oxytocin is implicated both in prosocial behavior and in the central nervous control of neuroendocrine responses to stress. This study was designed to determine the effects of social support and oxytocin on cortisol, mood, and anxiety responses to psychosocial stress in humans. METHODS In a placebo-controlled, double-blind study, 37 healthy men were exposed to the Trier Social Stress Test. All participants were randomly assigned to receive intranasal oxytocin (24 IU) or placebo 50 min before stress, and either social support from their best friend during the preparation period or no social support. RESULTS Salivary free cortisol levels were suppressed by social support in response to stress. Comparisons of pre- and poststress anxiety levels revealed an anxiolytic effect of oxytocin. More importantly, the combination of oxytocin and social support exhibited the lowest cortisol concentrations as well as increased calmness and decreased anxiety during stress. CONCLUSIONS Oxytocin seems to enhance the buffering effect of social support on stress responsiveness. These results concur with data from animal research suggesting an important role of oxytocin as an underlying biological mechanism for stress-protective effects of positive social interactions.

Single hadron response measurement and calorimeter jet energy scale uncertainty with the ATLAS detector at the LHC

The uncertainty on the calorimeter energy response to jets of particles is derived for the ATLAS experiment at the Large Hadron Collider (LHC). First, the calorimeter response to single isolated charged hadrons is measured and compared to the Monte Carlo simulation using proton-proton collisions at centre-of-mass energies of root s = 900 GeV and 7 TeV collected during 2009 and 2010. Then, using the decay of K-s and Lambda particles, the calorimeter response to specific types of particles (positively and negatively charged pions, protons, and anti-protons) is measured and compared to the Monte Carlo predictions. Finally, the jet energy scale uncertainty is determined by propagating the response uncertainty for single charged and neutral particles to jets. The response uncertainty is 2-5 % for central isolated hadrons and 1-3 % for the final calorimeter jet energy scale.

Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set

Stepwise uncertainty reduction (SUR) strategies aim at constructing a sequence of points for evaluating a function  f in such a way that the residual uncertainty about a quantity of interest progressively decreases to zero. Using such strategies in the framework of Gaussian process modeling has been shown to be efficient for estimating the volume of excursion of f above a fixed threshold. However, SUR strategies remain cumbersome to use in practice because of their high computational complexity, and the fact that they deliver a single point at each iteration. In this article we introduce several multipoint sampling criteria, allowing the selection of batches of points at which f can be evaluated in parallel. Such criteria are of particular interest when f is costly to evaluate and several CPUs are simultaneously available. We also manage to drastically reduce the computational cost of these strategies through the use of closed form formulas. We illustrate their performances in various numerical experiments, including a nuclear safety test case. Basic notions about kriging, auxiliary problems, complexity calculations, R code, and data are available online as supplementary materials.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

Networks of Information Exchange Under Uncertainty: The Case of Unconventional Gas Development in the United Kingdom

Modern policy-making is increasingly influenced by different types of uncertainty. Political actors are supposed to behave differently under the context of uncertainty then in “usual” decision-making processes. Actors exchange information in order to convince other actors and decision-makers, to coordinate their lobbying activities and form coalitions, and to get information and learn on the substantive issue. The literature suggests that preference similarity, social trust, perceived power and functional interdependence are particularly important drivers of information exchange. We assume that social trust as well as being connected to scientific actors is more important under uncertainty than in a setting with less uncertainty. To investigate information exchange under uncertainty analyze the case of unconventional shale gas development in the UK from 2008 till 2014. Our study will rely on statistical analyses of survey data on a diverse set of actors dealing with shale gas development and regulation in the UK.

Policy Instrument Selection under Uncertainty: The Case of Micropollution Regulation

The paper addresses the question of which factors drive the formation of policy preferences when there are remaining uncertainties about the causes and effects of the problem at stake. To answer this question we examine policy preferences reducing aquatic micropollutants, a specific case of water protection policy and different actor groups (e.g. state, science, target groups). Here, we contrast two types of policy preferences: a) preventive or source-directed policies, which mitigate pollution in order to avoid contact with water; and b) reactive or end-of-pipe policies, which filter water already contaminated by pollutants. In a second step, we analyze the drivers for actors’ policy preferences by focusing on three sets of explanations, i.e. participation, affectedness and international collaborations. The analysis of our survey data, qualitative interviews and regression analysis of the Swiss political elite show that participation in the policy-making process leads to knowledge exchange and reduces uncertainties about the policy problem, which promotes preferences for preventive policies. Likewise, actors who are affected by the consequences of micropollutants, such as consumer or environmental associations, opt for anticipatory policies. Interestingly, we find that uncertainties about the effectiveness of preventive policies can promote preferences for end-of-pipe policies. While preventive measures often rely on (uncertain) behavioral changes of target groups, reactive policies are more reliable when it comes to fulfilling defined policy goals. Finally, we find that in a transboundary water management context, actors with international collaborations prefer policies that produce immediate and reliable outcomes.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Synchronous and Time-Lagged Effects between Occupational Self-Efficacy and Objective and Subjective Career Success: Findings from a Four-Wave and 9-Year Longitudinal Study

We integrated research on the dimensionality of career success into social-cognitive career theory and explored the positive feedback loop between occupational self-efficacy and objective and subjective career success over time (self-efficacy → objective success → subjective success → self-efficacy). Furthermore, we theoretically accounted for synchronous and time-lagged effects, as well as indirect reciprocity between the variables. We tested the proposed model by means of longitudinal structural equation modeling in a 9-year four-wave panel design, by applying a model comparison approach and indirect effect analyses (N = 608 professionals). The findings supported the proposed positive feedback loop between occupational self-efficacy and career success. Supporting our time-based reasoning, the findings showed that unfolding effects between occupational self-efficacy and objective career success take more time (i.e., time-lagged or over time) than unfolding effects between objective and subjective career success, as well as between subjective career success and occupational self-efficacy (i.e., synchronous or concurrently). Indirect effects of past on future occupational self-efficacy via objective and subjective career success were significant, providing support for an indirect reciprocity model. Results are discussed with respect to extensions of social-cognitive career theory and occupational self-efficacy development over time.

Fostering networking behavior, career planning and optimism, and subjective career success: An intervention study

The present study evaluated personal resource-oriented interventions supporting the career development of young academics, working at German universities within the STEM fields. The study sought to foster subjective career success by improving networking behavior, career planning, and career optimism. The study involved a quasi-experimental pre-post intervention with two intervention and two control groups (N = 81 research associates). Participants of the first intervention group received networking training; participants of the second intervention group received the same networking training plus individual career coaching. Participants of both intervention groups were female. Participants of the control groups (i.e., male vs. female group) did not participate in any intervention. As expected, path analyses, based on mean differences frompre-test to post-test, revealed an increase in career planning and career optimism within the networking plus career coaching intervention group, that was indirectly positively related to changes in subjective career success. Contrary to our expectations, the networking group training alone and in combination with the career coaching showed no effectiveness in fostering networking behavior. Results are discussed in the context of career counseling and intervention effectiveness studies.

Gender, Professional Networks, and Subjective Career Success within Early Academic Science Careers: The Role of Gender Composition in Inside and Outside Departmental Support Networks

The present study analyzed (a) gender differences in the gender composition (i.e., the proportion of male to female contacts) of professional support networks inside and outside an individual’s academic department and (b) how these differences in gender composition relate to subjective career success (i.e., perceived career success and perceived external marketability). Results showed that the networks’ gender composition is associated with subjective career success. Men’s networks consist of a higher proportion of male to female supporters, which, in turn, was positively related to subjective career success. Additional analyses revealed that the findings could not be accounted for by alternative factors, such as network size, networking behaviors, and career ambition.