Background and Aims: Previous research has pointed to the potential of workplace interventions addressing alcohol consumption. However, there is still no systematic overview of the effects of these interventions. Therefore, we aimed to quantify the effectiveness of workplace interventions addressing alcohol use by conducting a meta‐analysis.Methods: A systematic literature search for randomized controlled trials of workplace alcohol interventions published between 1995 and 2020 was conducted in five databases. Studies were included if they were performed in the workplace and reported universal or selective interventions aiming for alcohol use reduction. Primary outcomes were any measures of alcohol use. Standardized mean effect sizes were used to calculate the meta‐analytic random‐effects‐model. Additional analyses were carried out to identify potential moderators and to examine the amount of heterogeneity and publication bias.Results: Twenty studies with 4484 participants were integrated into the meta‐analysis. Results revealed a significant overall mean effect indicating a reduction of alcohol use in favor of the treatment group (d = −0.16, 95% CI = [−0.2715; −0.0511]). Heterogeneity within the data structure was found to be moderate to substantial (I² = 75.9%, Q‐test P < 0.001, τ² = 0.0375). Additional moderator analyses only showed a significant effect for length of measurement period (P = 0.049).Conclusions: Alcohol‐related prevention programs conducted in the workplace have a statistically significant and favorable effect on alcohol consumption. Although the overall mean effect is considered to be small, it underlines the effectiveness of workplace interventions targeting a reduction in alcohol use.

Building on self-regulation research and self-determination theory, the present research investigates the causal effects of job demands and job control on physical activity after work. In 2 experiments (total N = 251), participants completed a work simulation that was followed by a physical activity task (cycling on a bicycle ergometer). We simulated a call center, consisting of customer interactions and calculation tasks. In both experiments, job demands (high vs. low) were manipulated between-subjects in terms of customer unfriendliness and task difficulty. In Experiment 2, we also manipulated job control (high vs. low) in terms of decision latitude. As predicted, both experiments showed negative effects of job demands on physical activity, reflecting that time on the bicycle ergometer was lower for individuals in the high demands condition compared to the low demands condition. However, this effect was not mediated by self-regulatory capacities. Regarding job control, we found preliminary evidence for the expected indirect effect on physical activity through increased self-determination. In summary, our results provide causal evidence for the influence of job characteristics on physical activity and, thereby, advance our understanding of the mechanisms underlying the spill-over of work into leisure time. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Previous research identified time spent in physical activity, sleeping, and working as predictors of emotional exhaustion. However, this research did not take into account the interdependence of these time-use components. Since daily time is limited to 24 h, time spent in one specific activity (e.g., sleep) cannot be used for any other activity (e.g., physical activity). We conducted a one-week daily sampling study to assess the compositional effects of physical activity, sleep, and work on emotional exhaustion. Since the sample consisted of 104 undergraduate students, work was operationalized as study time. Participants wore accelerometers for one week continuously to assess sleep and physical activity. Also, they filled in questionnaires on study time and emotional exhaustion every morning. Multilevel and compositional data analyses were conducted. The multilevel analysis revealed significant between- (p = 0.012) and within-level (p < 0.001) associations of study time with emotional exhaustion. The compositional approach showed that time spent in physical activity was negatively related to emotional exhaustion (p = 0.007), whereas time spent studying was positively related to emotional exhaustion (p = 0.003), relative to the remaining two time-use components. In conclusion, our results show that emotional exhaustion is not only associated with work-related factors, but also with off-job physical activity.

Building on the seminal studies of Solomon Asch and Muzafer Sherif, recent research has advanced our understanding of the mechanisms underlying social influence by applying a diffusion model analysis. Here, we combined the social identity approach to social influence with a diffusion model analysis to unravel the mechanisms underlying social influence. In particular, we aimed to disentangle whether the difference between in‐group and out‐group influence on perceptual decision‐making is driven by a judgmental bias (i.e., changes in decision criteria) or a perceptual bias (i.e., changes in the uptake of sensory information). Preregistered analyses indicated that in‐groups exerted stronger social influence than out‐groups because in‐groups induced a stronger perceptual bias than out‐groups. This finding is in line with the single process assumption of the social identity approach because it implicates that the single process driving social influence (i.e., self‐categorisation) translates into a change in a single subprocess of decision‐making (i.e., biased information uptake). In conclusion, our results highlight that our theoretical understanding of social influence can be expanded by integrating the social identity approach with a diffusion model analysis.