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When pandemic hits
(2020)
The governmental lockdowns related to the COVID-19 pandemic have forced people to change their behavior in many ways including changes in exercise. We used the brief window of global lockdown in the months of March/April/May 2020 as an opportunity to investigate the effects of externally imposed restrictions on exercise-related routines and related changes in subjective well-being. Statistical analyses are based on data from 13,696 respondents in 18 countries using a cross-sectional online survey. A mixed effects modeling approach was used to analyze data. We tested whether exercise frequency before and during the pandemic would influence mood during the pandemic. Additionally, we used the COVID-19 pandemic data to build a prediction model, while controlling for national differences, to estimate changes in exercise frequency during similar future lockdown conditions depending on prelockdown exercise frequency. According to the prediction model, those who rarely exercise before a lockdown tend to increase their exercise frequency during it, and those who are frequent exercisers before a lockdown tend to maintain it. With regards to subjective well-being, the data show that those who exercised almost every day during this pandemic had the best mood, regardless of whether or not they exercised prepandemic. Those who were inactive prepandemic and slightly increased their exercise frequency during the pandemic, reported no change in mood compared to those who remained inactive during the pandemic. Those who reduced their exercise frequency during the pandemic reported worse mood compared to those who maintained or increased their prepandemic exercise frequency. This study suggests that under similar lockdown conditions, about two thirds of those who never or rarely exercise before a lockdown might adopt an exercise behavior or increase their exercise frequency. However, such changes do not always immediately result in improvement in subjective well-being. These results may inform national policies, as well as health behavior and exercise psychology research on the importance of exercise promotion, and prediction of changes in exercise behavior during future pandemics.
According to coherence-based models of legal judgment, individuals form coherent mental representations to make sense of the available evidence. In this process, evidence supporting the emerging assessment is accentuated, resulting in coherence effects. Dependent on specific implementations of coherence-based models, in legal tasks both overweighting of evidence that is presented early (a primacy effect) or late (a recency effect) can be predicted. In two studies (N-1 = 221, N-2 = 332), we investigate coherence effects, order effects, and their interrelation in a mock legal case. We manipulate the order in which the evidence is presented, and whether or not individuals are induced to assess provisionally whether they deem the defendant guilty after seeing half of the evidence (leaning). This leaning manipulation should increase primacy effects. We consistently observed recency effects and no stronger influence of primacy effects when people indicate a leaning. Order and leaning did not influence the magnitude of coherence effects. In contrast to previous findings in consumer research and risky choice tasks, coherence effects did not mediate the effect of the order in which the evidence in legal judgments is presented. If our results hold more generally, coherence-based models of legal judgment might have to be revised to give more weight to recency effects as compared to the typically predicted primacy effects. This revision would have implications for the design of criminal procedure.
Recent research indicates that affective responses during exercise are an important determinant of future exercise and physical activity. Thus far these responses have been measured with standardized self-report scales, but this study used biometric software for automated facial action analysis to analyze the changes that occur during physical exercise. A sample of 132 young, healthy individuals performed an incremental test on a cycle ergometer. During that test the participants’ faces were video-recorded and the changes were algorithmically analyzed at frame rate (30 fps). Perceived exertion and affective valence were measured every two minutes with established psychometric scales. Taking into account anticipated inter-individual variability, multilevel regression analysis was used to model how affective valence and ratings of perceived exertion (RPE) covaried with movement in 20 facial action areas. We found the expected quadratic decline in self-reported affective valence (more negative) as exercise intensity increased. Repeated measures correlation showed that the facial action mouth open was linked to changes in (highly intercorrelated) affective valence and RPE. Multilevel trend analyses were calculated to investigate whether facial actions were typically linked to either affective valence or RPE. These analyses showed that mouth open and jaw drop predicted RPE, whereas (additional) nose wrinkle was indicative for the decline in affective valence. Our results contribute to the view that negative affect, escalating with increasing exercise intensity, may be the body’s essential warning signal that physiological overload is imminent. We conclude that automated facial action analysis provides new options for researchers investigating feelings during exercise. In addition, our findings offer physical educators and coaches a new way of monitoring the affective state of exercisers, without interrupting and asking them.
Recent research indicates that affective responses during exercise are an important determinant of future exercise and physical activity. Thus far these responses have been measured with standardized self-report scales, but this study used biometric software for automated facial action analysis to analyze the changes that occur during physical exercise. A sample of 132 young, healthy individuals performed an incremental test on a cycle ergometer. During that test the participants’ faces were video-recorded and the changes were algorithmically analyzed at frame rate (30 fps). Perceived exertion and affective valence were measured every two minutes with established psychometric scales. Taking into account anticipated inter-individual variability, multilevel regression analysis was used to model how affective valence and ratings of perceived exertion (RPE) covaried with movement in 20 facial action areas. We found the expected quadratic decline in self-reported affective valence (more negative) as exercise intensity increased. Repeated measures correlation showed that the facial action mouth open was linked to changes in (highly intercorrelated) affective valence and RPE. Multilevel trend analyses were calculated to investigate whether facial actions were typically linked to either affective valence or RPE. These analyses showed that mouth open and jaw drop predicted RPE, whereas (additional) nose wrinkle was indicative for the decline in affective valence. Our results contribute to the view that negative affect, escalating with increasing exercise intensity, may be the body’s essential warning signal that physiological overload is imminent. We conclude that automated facial action analysis provides new options for researchers investigating feelings during exercise. In addition, our findings offer physical educators and coaches a new way of monitoring the affective state of exercisers, without interrupting and asking them.