Advances in theory and research on self-regulation and decision-making processes have yielded important insights into how cognitive, emotional, and social processes shape risk perceptions and risk-related decisions. We examine how self-regulation theory can be applied to inform our understanding of decision-making processes within the context of genomic testing, a clinical arena in which individuals face complex risk information and potentially life-altering decisions. After presenting key principles of self-regulation, we present a genomic testing case example to illustrate how principles related to risk representations, approach and avoidance motivations, emotion regulation, defensive responses, temporal construals, and capacities such as numeric abilities can shape decisions and psychological responses during the genomic testing process. We conclude with implications for using self-regulation theory to advance science within genomic testing and opportunities for how this research can inform further developments in self-regulation theory.

Objective: Appetitive risk behaviors (ARB), including tobacco use, alcohol consumption, consumption of calorie dense/nutrient-poor foods, and sexual risk behavior contribute substantially to morbidity and mortality. Affective states that arise from a wide array of unrelated circumstances (i.e., incidental affect) may carry over to influence ARB. A meta-analysis is needed to systematically examine causal evidence for the role of incidental affect (including specific emotions) in influencing ARB. Method: Integrating effect sizes from 91 published and unpublished experimental studies that include both an incidental-affect induction and neutral-control condition (k = 271 effect sizes: k = 183 negative affect, k = 78 positive affect), this meta-analysis examines how negative and positive affective states influenced ARB and related health cognitions (e.g., intentions, evaluations, craving, perceived control). Results: Negative affective states reliably increased ARB, in analyses where all negative affective states were analyzed (d = .29) and in stratified analyses of just negative mood (d = .30) and stress (d = .48). These effects were stronger among study populations coded as clinically at risk. Positive affective states generally did not influence ARB or related health cognitions, except in the presence of a craving cue. Design issues of extant literature largely precluded conclusions about the effects of specific positive and negative affective states. Conclusion: Taken together, findings suggest the importance of strategies to attenuate negative affect incidental to ARB to facilitate healthier behavioral patterns, especially among clinically at-risk individuals. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Climate change poses a multifaceted, complex, and existential threat to human health and well-being, but efforts to communicate these threats to the public lag behind what we know how to do in communication research. Effective communication about climate change's health risks can improve a wide variety of individual and population health-related outcomes by: (1) helping people better make the connection between climate change and health risks and (2) empowering them to act on that newfound knowledge and understanding. The aim of this manuscript is to highlight communication methods that have received empirical support for improving knowledge uptake and/or driving higher-quality decision making and healthier behaviors and to recommend how to apply them at the intersection of climate change and health. This expert consensus about effective communication methods can be used by healthcare professionals, decision makers, governments, the general public, and other stakeholders including sectors outside of health. In particular, we argue for the use of 11 theory-based, evidence-supported communication strategies and practices. These methods range from leveraging social networks to making careful choices about the use of language, narratives, emotions, visual images, and statistics. Message testing with appropriate groups is also key. When implemented properly, these approaches are likely to improve the outcomes of climate change and health communication efforts.

Risk perception is an important construct in many health behavior theories. Smoking risk perceptions are thoughts and feelings about the harms associated with cigarette smoking. Wide variation in the terminology, definition, and assessment of this construct makes it difficult to draw conclusions about the associations of risk perceptions with smoking behaviors. To understand optimal methods of assessing adults' cigarette smoking risk perceptions (among both smokers and nonsmokers), we reviewed best practices from the tobacco control literature, and where gaps were identified, we looked more broadly to the research on risk perceptions in other health domains. Based on this review, we suggest assessments of risk perceptions (1) about multiple smoking-related health harms, (2) about harms over a specific timeframe, and (3) for the person affected by the harm. For the measurement of perceived likelihood in particular (ie, the perceived chance of harm from smoking based largely on deliberative thought), we suggest including (4) unconditional and conditional items (stipulating smoking behavior) and (5) absolute and comparative items and including (6) comparisons to specific populations through (7) direct and indirect assessments. We also suggest including (8) experiential (ostensibly automatic, somatic perceptions of vulnerability to a harm) and affective (emotional reactions to a potential harm) risk perception items. We also offer suggestions for (9) response options and (10) the assessment of risk perception at multiple time points. Researchers can use this resource to inform the selection, use, and future development of smoking risk perception measures.

Implications

Incorporating the measurement suggestions for cigarette smoking risk perceptions that are presented will help researchers select items most appropriate for their research questions and will contribute to greater consistency in the assessment of smoking risk perceptions among adults.

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations (claims) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms physical distancing and social distancing. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.