Alcohol (Hanover, York County, Pa.)最新文献

Background: A body of research suggests that many participants in alcohol use treatment research begin making changes to their drinking behavior prior to beginning treatment. Although several studies have examined factors associated with such change, results have been mixed or yielded small effects. Both theoretical and methodological limitations have hindered efforts to understand such change, highlighting the need for novel analytic approaches. This study aimed to examine how traditional modeling methods (linear and logistic regression) and machine learning (recursive partitioning, random forests, neural networks, and support vector machines) may be used to predict pretreatment drinking changes.

Methods: Using baseline psychological constructs and demographic variables of an existing dataset of 175 predominately white (93.7%) participants, we randomly split the data into a training dataset (80% of available data) and a testing dataset (20% of available data) and used the training dataset to run the initial models. Then, using the remaining test data, we used the trained models to make predictions on new data. We ran models predicting the percent change in drinking and heavy drinking days, and classification models on whether participants were pretreatment changers (50% reduction in drinking days; 70% reduction in percent heavy drinking days).

Results: Overall, the neural-network models tended to have the highest predictive accuracy, although the model areas under the curves ranged from poor to acceptable. Variable importance algorithms indicated that demographic factors (e.g., education and income) and psychological constructs (e.g., processes of change) were among the predictors that contributed most to improved model performance.

Conclusions: The identification of demographic variables as important predictors highlights the importance of understanding demographic and societal-level factors as potential drivers of pretreatment drinking changes.

Background: Uninformed attitudes toward prenatal alcohol exposure (PAE) and fetal alcohol spectrum disorder (FASD) remain barriers in the assessment and diagnosis of affected individuals. While there is a growing need to evaluate the attitudes of professionals who work closely with pregnant individuals and those living with FASD, it is currently unknown whether existing psychometric tools, quantifying such attitudes, are reliable. The psychometric properties of two measures were therefore investigated: the "Alcohol and Pregnancy Measure" (capturing attitudes toward PAE) and the "Knowledge and Attitudes Regarding FASD Measure" (capturing attitudes toward FASD).

Methods: A total of 1797 healthcare workers, recruited largely from the National Health Service in Scotland, completed both psychometric measures as part of a nationwide survey of attitudes. Participants also completed novel measurements of their knowledge of FASD and their attitudes toward the health advice for pregnant women regarding alcohol use during pregnancy.

Results: Support for the psychometric validity of the two measures was partially observed. Both measures were found to have multifactor structures, instead of the hypothesised one-factor solutions. Such multifactor structures demonstrated goodness-of-fit in confirmatory factor analyses. Moreover, both measures showed convergent validity with both knowledge of FASD and attitudes toward current health advice regarding PAE. Both the measures' sensitivity to ceiling effects and poor subfactor internal consistencies, however, remain a concern.

Conclusion: Results indicate that both measures, when used at the total-scale level, can acceptably assess attitudes toward PAE and FASD. Revisions are required at the item-level to further improve subfactor internal consistency and minimize ceiling effect likelihood.

Background: Binge drinking, defined as episodic alcohol intake reaching intoxication, is prevalent among young adults and linked to impaired cognitive control and risk for alcohol use disorder (AUD). Even though executive deficits contribute to addiction, evidence on their alterations in young adult binge drinkers (BDs) as a function of biological sex remains limited. This study examined cognitive and motor aspects of decision making as reflected in event-related theta and beta oscillations, respectively. The executive neurobehavioral markers were analyzed in conjunction with alcohol-related and other self-reported variables and cognitive functions in BDs versus light drinkers (LDs) of both sexes.

Methods: Sixty-eight participants (34 BDs, 34 LDs, 50% women) completed a modified Stroop task. Electroencephalography (EEG) signals were analyzed in the time-frequency domain with Morlet wavelets. Behavioral performance and event-related theta (4-7 Hz) and beta (15-25 Hz) power were compared across levels of conflict, drinking groups, and sex.

Results: BDs exhibited longer reaction times on high-conflict trials, reflecting compensatory slowing during cognitive interference. Lower conflict-induced theta power in BDs than in LDs was evident primarily among women. It was associated with greater alcohol intake and it mediated the impact of binge drinking on degraded task performance among BD women. In contrast, event-related beta desynchronization during response preparation was attenuated and delayed only in BD men, which was partially mediated by impulsivity.

Conclusions: The observed links between binge drinking and dysregulated cognitive and motor control processes were interpreted in regard to sex-specific neural alterations. Weaker engagement of cognitive control in BD women may be reflected in an impaired capacity to optimize decisions in challenging situations. Beta decrease during response preparation was especially attenuated in more impulsive BD men, which may contribute to their inability to refrain from risky behaviors. These findings highlight the need to consider biological sex in alcohol-related neurocognitive research.