The daily association between affect and alcohol use: a meta-analysis of individual participant data

Influential psychological theories hypothesize that people consume alcohol in response to the experience of both negative and positive emotions. Despite two decades of daily diary and ecological momentary assessment research, it remains unclear whether people consume more alcohol on days they experience higher negative and positive affect in everyday life. In this preregistered meta-analysis, we synthesized the evidence for these daily associations between affect and alcohol use. We included individual participant data from 69 studies (N = 12,394), which used daily and momentary surveys to assess affect and the number of alcoholic drinks consumed. Results indicate that people are not more likely to drink on days they experience high negative affect, but are more likely to drink and drink heavily on days high in positive affect. People self-reporting a motivational tendency to drink-to-cope and drink-to-enhance consumed more alcohol, but not on days they experienced higher negative and positive affect. Results were robust across different operationalizations of affect, study designs, study populations, and individual characteristics. These findings challenge the long-held belief that people drink more alcohol following increases in negative affect. Integrating these findings under different theoretical models and limitations of this field of research, we collectively propose an agenda for future research to explore open questions surrounding affect and alcohol use.The present study was funded by the Canadian Institutes of Health Research Grant MOP-115104 (Roisin M. O’Connor), Canadian Institutes of Health Research Grant MSH-122803 (Roisin M. O’Connor), John A. Hartford Foundation Grant (Paul Sacco), Loyola University Chicago Research Support Grant (Tracy De Hart), National Institute for Occupational Safety and Health Grant T03OH008435 (Cynthia Mohr), National Institutes of Health (NIH) Grant F31AA023447 (Ryan W. Carpenter), NIH Grant R01AA025936 (Kasey G. Creswell), NIH Grant R01AA025969 (Catharine E. Fairbairn), NIH Grant R21AA024156 (Anne M. Fairlie), NIH Grant F31AA024372 (Fallon Goodman), NIH Grant R01DA047247 (Kevin M. King), NIH Grant K01AA026854 (Ashley N. Linden-Carmichael), NIH Grant K01AA022938 (Jennifer E. Merrill), NIH Grant K23AA024808 (Hayley Treloar Padovano), NIH Grant P60AA11998 (Timothy Trull), NIH Grant MH69472 (Timothy Trull), NIH Grant K01DA035153 (Nisha Gottfredson), NIH Grant P50DA039838 (Ashley N. Linden-Carmichael), NIH Grant K01DA047417 (David M. Lydon-Staley), NIH Grant T32DA037183 (M. Kushner), NIH Grant R21DA038163 (A. Moore), NIH Grant K12DA000167 (M. Potenza, Stephanie S. O’Malley), NIH Grant R01AA025451 (Bruce Bartholow, Thomas M. Piasecki), NIH Grant P50AA03510 (V. Hesselbrock), NIH Grant K01AA13938 (Kristina M. Jackson), NIH Grant K02AA028832 (Kevin M. King), NIH Grant T32AA007455 (M. Larimer), NIH Grant R01AA025037 (Christine M. Lee, M. Patrick), NIH Grant R01AA025611 (Melissa Lewis), NIH Grant R01AA007850 (Robert Miranda), NIH Grant R21AA017273 (Robert Miranda), NIH Grant R03AA014598 (Cynthia Mohr), NIH Grant R29AA09917 (Cynthia Mohr), NIH Grant T32AA07290 (Cynthia Mohr), NIH Grant P01AA019072 (P. Monti), NIH Grant R01AA015553 (J. Morgenstern), NIH Grant R01AA020077 (J. Morgenstern), NIH Grant R21AA017135 (J. Morgenstern), NIH Grant R01AA016621 (Stephanie S. O’Malley), NIH Grant K99AA029459 (Marilyn Piccirillo), NIH Grant F31AA022227 (Nichole Scaglione), NIH Grant R21AA018336 (Katie Witkiewitz), Portuguese State Budget Foundation for Science and Technology Grant UIDB/PSI/01662/2020 (Teresa Freire), University of Washington Population Health COVID-19 Rapid Response Grant (J. Kanter, Adam M. Kuczynski), U.S. Department of Defense Grant W81XWH-13-2-0020 (Cynthia Mohr), SANPSY Laboratory Core Support Grant CNRS USR 3413 (Marc Auriacombe), Social Sciences and Humanities Research Council of Canada Grant (N. Galambos), and Social Sciences and Humanities Research Council of Canada Grant (Andrea L. Howard)

Interaction and Nonlinear Effects of Temperament Reactivity and Regulation on Adjustment Problems in Preadolescence

Thesis (Master's)--University of Washington, 2019Dimensions of child temperament related to reactivity and self-regulation are often studied in relation to child adjustment. Yet despite theory on interactive and nonlinear effects of dimensions of temperament, few studies have tested these. We examined quadratic and interactive effects of child temperament on adjustment. Interactive and nonlinear effects of temperament (fear, frustration, and executive control) on emotional and behavioral problems (anxiety, depression, and conduct problems) were investigated in a sample of 214 children aged 9-13. Using behavioral measures of temperament, we predicted concurrent problems, as well as problems one year later in hierarchical regression analyses. Executive control was consistently related to fewer problems. We observed a quadratic effect of executive control on depression, and an interaction between frustration and executive control predicting conduct problems. Low levels of executive control were associated with heightened risk for concurrent depression, whereas moderate to high levels of executive control were associated with similarly low levels of depression. Higher executive control was associated with fewer conduct problems for those moderate to high in frustration, and unassociated with conduct problems for those low in frustration. Examination of interactive and nonlinear effects of dimensions of temperament can clarify understanding of risk for child adjustment problems

Characterizing the Role of Avoidant Coping in the Development of Drinking to Cope with Negative Emotions

Thesis (Ph.D.)--University of Washington, 2022The present study integrates coping research into a current model of alcohol use disorders etiology, with the goal of better articulating the development of alcohol use disorders. Research has shown that an emotional and impulsive temperament may predispose a person to later risky drinking, and further that much of this risk is due to an increased likelihood of learning that alcohol is an effective means of reducing negative emotions (termed coping expectancies). The Acquired Preparedness (AP; Anderson & Smith, 2001) model formalizes this idea into a testable mediational model; however, the mechanisms which connect personality to the development of coping expectancies is not well-understood. Aim 1 of the was to extend the AP model to include coping constructs, particularly avoidant coping, providing an important test of the longitudinal mechanisms by which early-appearing temperament characteristics convey risk for outcomes in adulthood. Aim 2 of the current dissertation was to understand the relations of avoidant coping to the development of coping expectancies. I hypothesized that 1) avoidant coping would predict coping expectancies, and that avoidant coping would be preceded by emotionality and impulsivity. I further hypothesized that 2) avoidant coping would predict trajectories of coping expectancies, and 3) individuals who have the most opportunities to learn the affect regulation function of alcohol – through their own drinking and through seeing peers drink – will have the strongest link between avoidant coping and coping expectancies. I tested these hypotheses in a large longitudinal dataset (n=454) following adolescents from age 13 into their 30s. The Adult and Family Development Project (AFDP, PI: Chassin) was a comprehensive longitudinal evaluation of individual, contextual, and familial factors related to the development of alcohol use and beliefs. Approximately half of the children in the sample lived with a parent with a history of alcohol use disorder at the time of the first assessment. In Aim 1 analyses, I did not find support for hypothesis 1. Although I found mixed evidence for emotionality and impulsivity predicting avoidant coping, avoidant coping was unrelated to later coping expectancies. In Aim 2 analyses, I found mixed support for hypothesis 2; avoidant coping predicted levels of coping expectancies, but not growth. In Aim 3 analyses, I did not find evidence for moderation of the avoidant coping-coping expectancy relation by peer use or age at first drink. However, peer use was a strong predictor of overall coping expectancies. In sum, avoidant coping was related to personality and to coping expectancies, but did not exhibit a mediated effect in direct tests of the extended AP model. Implications for the role of coping in the etiology of alcohol use disorders are discussed

Negative urgency is correlated with the use of reflexive and disengagement emotion regulation strategies

Negative urgency predicts both internalizing and externalizing psychopathology. Although it is hypothesized that urgency is characterized by reflexive responses to negative emotion that focus on immediate relief from distress, little research has addressed this hypothesis. Using data from four independent samples of adolescents and college students (n=1,268), we estimated the association between trait negative urgency and emotion regulation strategies that reflect either reflexive responses or disengagement. We verified these effects in two samples ecological momentary assessments (EMA) (n=198). In retrospective data, negative urgency was correlated with using more disengagement or reflexive emotion regulation strategies relative to engagement strategies (r=.39; .38, 95% CI =0.30–0.49; 0.18–0.57). This finding replicated in EMA data (r=.24, 95% CI =0.11–0.38). Emotion regulation may be a key mechanism of the effects of urgency on psychopathology. Interventions targeting emotion regulation among those high on urgency may be warranted

A state model of negative urgency: Do momentary reports of emotional impulsivity reflect global self-report?

In a sample of young adults (N = 222) assessed 3 times per day for 10 days, we tested whether negative emotions were associated with multiple facets of impulsivity at the state-level, and whether those associations were moderated by global self-report of negative urgency. Our findings suggest a robust within-person association between negative affect and acting on impulse. However, global self-report of negative urgency did not moderate any emotion-impulsivity association we tested

Making sense of some odd ratios: A tutorial and improvements to present practices in reporting and visualizing quantities of interest for binary and count outcome models

Objective: Generalized linear models (GLMs) such as logistic and Poisson regression are among the most common statistical methods for modeling binary and count outcomes. Though single-coefficient tests (odds ratios, incidence rate ratios) are the most common way to test predictor-outcome relations in these models, they provide limited information on the magnitude and nature of relations with outcomes. We assert that this is largely because they do not describe direct relations with quantities of interest (QoIs) such as probabilities and counts. Shifting focus to QoIs makes several nuances of GLMs more apparent. Method: To bolster interpretability of these models, we provide a tutorial on logistic and Poisson regression and suggestions for enhancements to current reporting practices for predictor-outcome relations in GLMs. Results: We first highlight differences in interpretation between traditional linear models and GLMs, and describe common misconceptions of GLMs. In particular, we highlight that link functions A) introduce non-constant relations between predictors and outcomes and B) make predictor-QoI relations dependent on other covariates. Each of these properties causes interpretation of GLM coefficients to diverge from interpretations of linear models. Next, we argue for a more central focus on QoIs (probabilities and counts). Finally, we propose and provide graphics and tables, with sample R code, for enhancing presentation and interpretation of QoIs. Conclusions: By improving present practices in the reporting of predictor-outcome relations in GLMs, we hope to maximize the amount of actionable information generated by statistical analyses and provide a tool for building a cumulative science of substance use disorders. Public Health Significance: We propose several enhancements to current reporting practices for statistical analyses of binary outcomes (e.g., psychiatric diagnoses) and count outcomes (e.g., number of alcoholic drinks consumed). We encourage researchers to interpret results in terms of quantities of interest (probabilities for binary models, counts for count models) and provide a tutorial and R code for implementing these analyses. Doing so can provide richer information about a statistical analysis, make study results easier for research consumers to understand, and facilitate comparison of results across studies

Interpreting interaction effects in generalized linear models of nonlinear probabilities and counts

Psychology research frequently involves the study of probabilities and counts. These are typically analyzed using generalized linear models (GLMs), which can produce these quantities via nonlinear transformation of model parameters. Interactions are central within many research applications of these models. To date, typical practice in evaluating interactions for probabilities or counts extends directly from linear approaches, in which evidence of an interaction effect is supported by using the product term coefficient between variables of interest. However, unlike linear models, interaction effects in GLMs describing probabilities and counts are not equal to product terms between predictor variables. Instead, interactions may be functions of the predictors of a model, requiring non-traditional approaches for interpreting these effects accurately. Here, we define interactions as change in a marginal effect of one variable as a function of change in another variable, and describe the use of partial derivatives and discrete differences for quantifying these effects. Using guidelines and simulated examples, we then use these approaches to describe how interaction effects should be estimated and interpreted for GLMs on probability and count scales. We conclude with an example using the Adolescent Brain Cognitive Development Study demonstrating how to correctly evaluate interaction effects in a logistic model