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Importance Alcohol consumption (AC) leads to death and disability worldwide. Ongoing discussions on potential negative effects of the COVID-19 pandemic on AC need to be informed by real-world evidence.
Objective To examine whether lockdown measures are associated with AC and consumption-related temporal and psychological within-person mechanisms.
Design, Setting, and Participants This quantitative, intensive, longitudinal cohort study recruited 1743 participants from 3 sites from February 20, 2020, to February 28, 2021. Data were provided before and within the second lockdown of the COVID-19 pandemic in Germany: before lockdown (October 2 to November 1, 2020); light lockdown (November 2 to December 15, 2020); and hard lockdown (December 16, 2020, to February 28, 2021).
Main Outcomes and Measures Daily ratings of AC (main outcome) captured during 3 lockdown phases (main variable) and temporal (weekends and holidays) and psychological (social isolation and drinking intention) correlates.
Results Of the 1743 screened participants, 189 (119 [63.0%] male; median [IQR] age, 37 [27.5-52.0] years) with at least 2 alcohol use disorder (AUD) criteria according to the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) yet without the need for medically supervised alcohol withdrawal were included. These individuals provided 14 694 smartphone ratings from October 2020 through February 2021. Multilevel modeling revealed significantly higher AC (grams of alcohol per day) on weekend days vs weekdays (β = 11.39; 95% CI, 10.00-12.77; P < .001). Alcohol consumption was above the overall average on Christmas (β = 26.82; 95% CI, 21.87-31.77; P < .001) and New Year’s Eve (β = 66.88; 95% CI, 59.22-74.54; P < .001). During the hard lockdown, perceived social isolation was significantly higher (β = 0.12; 95% CI, 0.06-0.15; P < .001), but AC was significantly lower (β = −5.45; 95% CI, −8.00 to −2.90; P = .001). Independent of lockdown, intention to drink less alcohol was associated with lower AC (β = −11.10; 95% CI, −13.63 to −8.58; P < .001). Notably, differences in AC between weekend and weekdays decreased both during the hard lockdown (β = −6.14; 95% CI, −9.96 to −2.31; P = .002) and in participants with severe AUD (β = −6.26; 95% CI, −10.18 to −2.34; P = .002).
Conclusions and Relevance This 5-month cohort study found no immediate negative associations of lockdown measures with overall AC. Rather, weekend-weekday and holiday AC patterns exceeded lockdown effects. Differences in AC between weekend days and weekdays evinced that weekend drinking cycles decreased as a function of AUD severity and lockdown measures, indicating a potential mechanism of losing and regaining control. This finding suggests that temporal patterns and drinking intention constitute promising targets for prevention and intervention, even in high-risk individuals.
A mechanism known as Pavlovian-to-instrumental transfer (PIT) describes a phenomenon by which the values of environmental cues acquired through Pavlovian conditioning can motivate instrumental behavior. PIT may be one basic mechanism of action control that can characterize mental disorders on a dimensional level beyond current classification systems. Therefore, we review human PIT studies investigating subclinical and clinical mental syndromes. The literature prevails an inhomogeneous picture concerning PIT. While enhanced PIT effects seem to be present in non-substance-related disorders, overweight people, and most studies with AUD patients, no altered PIT effects were reported in tobacco use disorder and obesity. Regarding AUD and relapsing alcohol-dependent patients, there is mixed evidence of enhanced or no PIT effects.
Additionally, there is evidence for aberrant corticostriatal activation and genetic risk, e.g., in association with high-risk alcohol consumption and relapse after alcohol detoxification. In patients with anorexia nervosa, stronger PIT effects elicited by low caloric stimuli were associated with increased disease severity.
In patients with depression, enhanced aversive PIT effects and a loss of action-specificity associated with poorer treatment outcomes were reported. Schizophrenic patients showed disrupted specific but intact general PIT effects. Patients with chronic back pain showed reduced PIT effects.
We provide possible reasons to understand heterogeneity in PIT effects within and across mental disorders. Further, we strengthen the importance of reliable experimental tasks and provide test-retest data of a PIT task showing moderate to good reliability.
Finally, we point toward stress as a possible underlying factor that may explain stronger PIT effects in mental disorders, as there is some evidence that stress per se interacts with the impact of environmental cues on behavior by selectively increasing cue-triggered wanting.
To conclude, we discuss the results of the literature review in the light of Research Domain Criteria, suggesting future studies that comprehensively assess PIT across psychopathological dimensions.
Background:
Prejudices against minorities can be understood as habitually negative evaluations that are kept in spite of evidence to the contrary. Therefore, individuals with strong prejudices might be dominated by habitual or "automatic" reactions at the expense of more controlled reactions. Computational theories suggest individual differences in the balance between habitual/model-free and deliberative/model-based decision-making.
Methods:
127 subjects performed the two Step task and completed the blatant and subtle prejudice scale.
Results:
By using analyses of choices and reaction times in combination with computational modeling, subjects with stronger blatant prejudices showed a shift away from model-based control. There was no association between these decision-making processes and subtle prejudices.
Conclusion:
These results support the idea that blatant prejudices toward minorities are related to a relative dominance of habitual decision-making. This finding has important implications for developing interventions that target to change prejudices across societies.
Mobile data collection of cognitive-behavioral tasks in substance use disorders: Where are we now?
(2022)
Introduction: Over the last decades, our understanding of the cognitive, motivational, and neural processes involved in addictive behavior has increased enormously. A plethora of laboratory-based and cross-sectional studies has linked cognitive-behavioral measures to between-subject differences in drinking behavior. However, such laboratory-based studies inevitably suffer from small sample sizes and the inability to link temporal fluctuations in task measures to fluctuations in real-life substance use. To overcome these problems, several existing behavioral tasks have been transferred to smartphones to allow studying cognition in the field. Method: In this narrative review, we first summarize studies that used existing behavioral tasks in the laboratory and self-reports of substance use with ecological momentary assessment (EMA) in the field. Next, we review studies on psychometric properties of smartphone-based behavioral tasks. Finally, we review studies that used both smartphone-based tasks and self-reports with EMA in the field. Results: Overall, studies were scarce and heterogenous both in tasks and in study outcomes. Nevertheless, existing findings are promising and point toward several methodological recommendations: concerning psychometrics, studies show that - although more systematic studies are necessary - task validity and reliability can be improved, for example, by analyzing several measurement sessions at once rather than analyzing sessions separately. Studies that use tasks in the field, moreover, show that power can be improved by choosing sampling schemes that combine time-based with event-based sampling, rather than relying on time-based sampling alone. Increasing sampling frequency can further increase power. However, as this also increases the burden to participants, more research is necessary to determine the ideal sampling frequency for each task. Conclusion: Although more research is necessary to systematically study both the psychometrics of smartphone-based tasks and the frequency at which task measures fluctuate, existing studies are promising and reveal important methodological recommendations useful for researchers interested in implementing behavioral tasks in EMA studies.