Do changes in cognitive control and decision making predict changes in drinking behavior?
Creators: name: Zech, Hilmar
name: Lee, Ying
name: Ebner-Priemer, Ulrich
name: Heinz, Andreas
name: Marxen, Michael
name: rapp, michael
name: Reichert, Markus
name: Tost, Heike
name: Dolan, Raymond
name: Smolka, Michael
name: Deserno, Lorenz
name: Garbusow, Maria
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Social and Behavioral Sciences

description: Alcohol use is one of the leading risk factors for death and disability (GBD 2016 Alcohol Collaborators, 2018). Loss of control over alcohol consumption has been linked to decreased cognitive control (Beck et al., 2012; Koob &amp; Volkow, 2010; Stavro et al., 2013; Gan et al Biol Psychiatry 2014)—a construct that includes, for example, the ability to inhibit unhealthy impulses, the ability to avoid excessive risk, and the ability to focus on one’s goals when distracted (Diamond, 2013; Powell et al., 2017). So far, this research has mostly focused on interindividual differences in cognitive control and alcohol consumption by studying the relationship cross-sectionally in the laboratory setting. Recent studies indicate, however, that cognitive control fluctuates significantly within individuals and that these fluctuations can predict changes in health behaviors. In the domain of eating, for example, Powell et al. (2017) showed that within-subject changes in inhibitory control can predict daily changes in snacking behavior. In patients with substance use disorder, Konova et al. (2020) showed that fluctuations in risky decision making can predict prospective opioid use. Given these findings and that changes in control over alcohol consumption cannot sensibly be tracked inside the laboratory (Ekhtiari et al., 2017; Sobell et al., 2000), it is, thus, crucial to understand how changes in cognitive control within individuals relate to changes in their real-life alcohol consumption. The overall goal of this study is to determine whether experimental measures of cognitive control and decision making relate to real-life changes in drinking behavior (Heinz et al., 2020). The key variables of interest are repeatedly collected on a smartphone, which enables us to address our main question of how within-subject changes in cognitive control and decision-making relate to variability in drinking behavior as assessed in real life. As an additional goal, we will also assess whether cognitive control protects against risk factors that are known to increase drinking—such as stress (Spanagel et al., 2014). To measure drinking behavior, participants indicate their alcohol consumption once every two days over a period of one year using Ecological Momentary Assessment (EMA) on the smartphone. Over the same period, participants also complete four behavioral tasks on the smartphone that measure cognitive control and decision making—two times at baseline and then once every month (14 times in total). We will assess the relationship between task variables and alcohol consumption separately in two datasets: The baseline data (here we will focus on between-subject effects) and the longitudinal data (here we will focus on both between and within-subject effects). All assessments will be done in two samples, one initial sample (N = 300) to test a priori hypotheses and explore the data and a second, independent sample (300 &lt; N &lt; 600) to replicate our findings (see Table 1). Additional objectives will be addressed: firstly, we will assess the validity of the behavioral tasks by comparing them to independent measures obtained in the lab collected in the same individuals as part an overarching project (the collaborative research center on “Loosing and regaining control over drug intake”; Heinz et al., 2020). Secondly, we will also assess the tasks’ internal consistency and test-retest reliability both in baseline and longitudinal datasets. Table 1 Sample sizes Sample Initial sample Replication sample Dataset Baseline (2 x 4 tasks) N = 300 300 &lt; N &lt; 600 Longitudinal (12 x 4 tasks) N = 300 300 &lt; N &lt; 600 In sum, this pre-registration has three objectives: 1) To assess the relationship between cognitive control and drinking at baseline (between-subject effects) 2) To assess the relationship between cognitive control and drinking longitudinally (between- and within-subject effects). 3) To assess the validity and reliability of the behavioral tasks. Analysis Plan Sample sizes, stopping rule, and state of data collection The initial sample will consist of 300 participants. The replication sample will consist of up to 600 participants (because the overarching project aims to collect up to N=900, Heinz et al., 2020; see Table 1). Should this sample size not be reached, we will analyze the replication sample on 30.06.2023 (end of the first funding period of the overarching project). More data might be collected beyond 30.06.2023 should the funding of the overarching project be extended for a second funding period. For the initial sample, baseline data and partial longitudinal data from 290 participants have already been collected. For the replication sample, no data has been collected. We have not yet begun to analyze any of the task data. Hypotheses Note that all analyses will be run on two independent samples: The initial sample and on the replication sample. Baseline hypotheses will be tested using Model 1 and longitudinal hypotheses will be tested using Model 2 (for model specification, see significance tests section). For the replication sample, additional hypotheses—based on analyses of the initial sample—might be added to this preregistration at a later stage. Hypotheses: baseline dataset C1. Baseline differences in inhibitory control relate negatively to drinking. C2. Baseline differences in risk aversion for gains relate negatively to drinking. C3. Baseline differences in risk aversion for losses relate positively to drinking. C4. Baseline differences in loss aversion relate negatively to drinking. C5. Baseline differences in sampling biases relate negatively to drinking. C6. Baseline differences in working memory relate negatively to drinking. C7. Baseline differences in working memory moderate stress-related increase in drinking behavior such that positive relation between stress and drinking is stronger when working memory is low. Hypotheses: longitudinal dataset L1. Changes in inhibitory control relate negatively to real-life changes in drinking. L2. Changes in risk aversion for gains relate negatively to real-life changes in drinking. L3. Changes in risk aversion for losses relate positively to real-life changes in drinking. L4. Changes in loss aversion relate negatively to real-life changes in drinking. L5. Changes in sampling biases relate negatively to real-life changes in drinking. L6. Between-subject differences in working memory moderate the effects of changes in inhibitory control and risk aversion on changes in drinking. Specifically, the expected positive and negative relationships between changes in inhibitory control as well as in risk aversion (see L1, L2, and L3) and drinking are stronger when working memory is low. L7. Between-subject differences in working memory moderate the effects of stress on drinking such that the positive relationship between stress and drinking is stronger when working memory is low. Predictor variables Working memory will be operationalized as working memory capacity (WMC) measured with a working memory task (McNab et al, 2015; see Table 2). Inhibitory control will be operationalized as stop signal reaction times (SSRTs) measured with a response inhibition (stop signal) task (Smittenaar et al., 2015). Variables related to risk aversion for gains/losses (PCG/PCL) will be operationalized as the percentage certain choices participants take in a risk-taking task (on gain/loss trials; Rutledge et al., 2014). Another specific aspect of risk aversion will be operationalized by the information sampling bias (SB), defined as how much participants oversample information (relative to optimal) in an information sampling task (Hunt et al., 2016). A variable related to loss aversion (PCM) will be operationalized as the percentage certain outcomes participants choose on mixed trials of the risk-taking task. Stress will be operationalized daily stress in the month following the behavioral tasks, as measured by the individual-stress-questionnaire (collected at the same time as the drinking measures; Perrez et al., 2000). Behavioral tasks will be completed by participants once every four weeks. Note, however, that the duration between tasks performances may vary according to sampling rule. This is defined as the following: Before the first longitudinal session, participants chose on which weekday and during which time they are least likely to drink. Every four weeks they are then alerted to complete the tasks at the predefined time. If at that time, they are intoxicated, hungover, or miss the alert, they will be triggered again at the pre-defined time on the next day. In this case, a button will also become available allowing participants to start the tasks without having to wait for the alarm. Should participants delay the tasks the next session will nevertheless start four weeks after the initial (missed) trigger. Aggregation. In the baseline analyses, we will take the mean of the outcomes from the two baseline measures of each behavioral task. We will, in addition, repeat analyses separately for each measurement. According to Neuhaus and Kalbfleisch (1998), variables in longitudinal datasets can have distinct between-subject and within-subject effects, which are not distinctly captured by standard mixed models. As our hypotheses involving WMC only predict effects based on between-subject differences, we will, therefore, take the by-participant mean WMC in the longitudinal data. Centering. In the between-subject analyses, all seven predictors (SSRT, PCG, PCL, PCM, SB, stress, WMC) will be centered around the grand mean. In the within-subject analysis, six predictors (SSRT, PCG, PCL, PCM, SB, stress) will be centered around the participant mean. WMC (which is aggregated by participant; see aggregation), will instead be centered around the grand mean. Outliers and exclusions. Across all behavioral tasks, trials with unrealistically low reaction times (&lt;150ms) will be excluded. Table 2 Outcome variables per task Task (subtask) Confirmatory outcome variable Additional outcome variables Working memory Working memory capacity (WMC) Encoding distraction costs Delayed distraction costs RTs Inhibitory control Stop signal reaction times (SSRTs) Correct Go responses Incorrect noGo responses RTs Risk aversion for gains Percentage certain in gain trials (PCG) RTs Risk aversion for losses Percentage certain in loss trials (PCM) RTs Loss aversion Percentage certain in mixed trials (PCM) RTs Information sampling Information oversampling relative to optimal (SB) Correct guesses RTs Dependent variable In the baseline analyses, the dependent variable will be the typical amount of alcohol consumed in the three months before the baseline measurement. In the longitudinal analyses, the dependent variable will be units of alcohol consumed in the month after each behavioral task was completed. Note that the dependent variable in the baseline-analyses differs slightly from that of the longitudinal analyses. The reason for this is that, at baseline, we do not have EMA-based alcohol consumption. Instead, we calculate consumption based on the Quantity-Frequency questionnaire. Significance tests All predictor variables will be added into a regression model (baseline data; see Model 1) or a mixed model (longitudinal data; see Model 2) with drinking as the outcome. Working memory will be allowed to interact with other predictors. After running these first models, covariates (age/gender) will be added to secondary models. As suggested by Barr et al. (2013), we will first run the model with the maximal random effects structure and remove random terms, in case the model does not converge. Note that an alternative approach was suggested by Matuschek et al. (2017). Here, the by-participant random structure of the mixed model is determined using model comparisons based on Akaike Information Criteria (Akaike, 1998). Significance tests will be based on p-values. Bootstrapped confidence intervals will also be reported. Model 1 Baseline model DR ~ (SSRT + PCG + PCL + PCM + ISB) + (WMC * STRESS) Note. The baseline model in Wilkinson-Pinheiro-Bates notation (Bates, 2005). The model includes baseline drinking (DR), baseline stop signal reaction times (SSRT), baseline percentage gambles for gains (PCG) and losses (PCL), baseline information sampling bias (SB), baseline loss aversion (PCM), baseline working memory (WMC), and baseline stress (STRESS). The covariates (age and gender) and their interactions will be added if they improve the model fit (based on BICs). Model 2 Longitudinal model DR ~ (SSRT + PCG + PCL + ISB + STRESS) * WMC + PCM + (1 + (SSRT + PCG + PCL + ISB + STRESS) * WMC + PCM | pp) Note. The longitudinal model in Wilkinson-Pinheiro-Bates notation (Bates, 2005). The model includes the within-subject components of cumulative drinking (DR), stop signal reaction times (SSRTs), percentage gambles for gains (PCG), percentage gambles for losses (PCL), information sampling biases (SB), and loss aversion (PCM), as well as the between-subject component of working memory capacity (WMC). In addition, it includes maximal by-participant (pp) random effects. The covariates (age and gender) and their interactions will be added if they improve the model fit (based on BICs). Validity and Reliability We will validate smartphone-based behavioral task outcomes by correlating them with similar tasks collected for the overarching project on participants own computers or in the laboratory (see Table 3) . We will also test correlations between behavioral task outcomes and various neuropsychological measurements collected by a separate project in the lab. For a full overview of additional tasks see Appendix A. We will also assess both internal consistency and test-retest reliabilities of each task—both in the baseline and in the longitudinal data. Internal consistency will be assessed based on Spearman-Brown-corrected split-half reliabilities within each session (based on odd-even splits as well as random splits). Assessing test-retest reliability in the baseline data is done using intra-class correlation coefficients (ICCs; specific ICCs will be chosen following the method of Liljequist, 2019). For the working memory task, an internal reliability cannot be computed because the task is adaptive and ends once participants reach their performance level. Therefore, splitting the task into two halves is not appropriate (Draheim et al., 2020). In the longitudinal data, reliability can be calculated through the McDonald’s Omega when using multilevel confirmatory factor analyses (Geldhof et al, 2014), which may be considered as part of exploratory analyses. Table 3 Validation tasks Smartphone Task variable Lab-based Validation tasks Working memory capacity (WMC) Digit Span Forward and Backward Task N-Back Stop signal reaction times (SSRTs) Stop Signal Task Stroop Task Risk aversion for gains Value-based decision making battery Cambridge Gambling task Risk aversion for losses Value-based decision making battery Loss aversion Value-based decision making battery Information oversampling relative to optimal Value-based decision making battery Exploratory analyses Cross-sectional and longitudinal components. In the exploratory analyses, we will split all predictors into their between-subject and within-subject components (using the method suggest by Neuhaus and Kalbfleisch, 1998). Time-lagged models. In the longitudinal data, we will also explore time-lagged models (e.g., Konova et al. 2020). To test whether tasks are more strongly associated with previous or subsequent drinking behavior, we will shift the dependent variable forward or backward. Here, we will also vary the time windows in which we will predict drinking behavior (e.g., from five to 30 days). Latent change score models. In addition to running mixed models, we will analyze the data using latent change score models (Kievit et al., 2018). Binge drinking. In addition to modelling cumulative drinking, we will also model binge drinking. Binge drinking will be operationalized as the total number of binge drinking events in the same calendar month in which the behavioral tasks were completed. A binge drinking event is defined as the consumption of at least 60 grams of alcohol (5 drinks) on one occasion for men and 48 grams (4 drinks) for women (Nebe et al., 2017; World Health Organization, 2018). Reducing number of predictors. Each of the behavioral tasks has several outcome variables (see Table 2). In the confirmatory analyses, we have chosen the outcome measure which best represents the relevant construct for testing our hypotheses (e.g. WMC for working memory; with the exception of the risk taking task which has three outcome variables). In the exploratory analyses, we will run factor analyses (FAs) instead to explore whether outcomes of different tasks load on common factors. In the longitudinal analyses, we will run exploratory, multilevel FAs or a low number of pre-specified latent change score models to account for the within-participants design. Genetic risk scores. The most recent polygenic risk scores for smoking behavior (Liu et al. 2019) and cognition (Savage et al. 2018; Lee et al. 2018) will be included as covariates. Variability and fluctuations. We will also analyze several measures of variability and fluctuations of drinking behavior. For this we will calculate the degree of fluctuation (F), the degree of difference (D), and the mean of the squared successive differences (MSSD) as described in Schiepek and Struk (2010) and Jahng (2008). Computational modelling. In addition to the task variables defined above, we will also calculate task variables based on computational modelling as described in Bernhard et al. (2017) and Rutledge et al. (2014). Potential differences between initial and replication sample. We do not expect any do not expect any sample- or time-specific effects between the initial and the replication sample. However, as the initial sample has unexpectedly been collected during the ongoing corona pandemic, it is possible that the initial sample is more effected by external factors than the replications sample. We will only be able to assess such effects systematically after all data collection will be completed (see section “Sample sizes, stopping rule, and state of data collection”). 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