Examining new-generation transdermal alcohol biosensor performance across laboratory and field contexts.

BACKGROUND: Wrist-worn transdermal alcohol sensors have the potential to change how alcohol consumption is measured. However, hardware and data analytic challenges associated with transdermal sensor data have kept these devices from widespread use. Given recent technological and analytic advances, this study provides an updated account of the performance of a new-generation wrist-worn transdermal sensor in both laboratory and field settings. METHODS: This work leverages machine learning models to convert transdermal alcohol concentration data into estimates of Breath Alcohol Concentration (BrAC) in a large-scale laboratory sample (N = 256, study 1) and a pilot field sample (N = 27, study 2). Specifically, in both studies, the accuracy of the translation is evaluated by comparing BAC estimates yielded by BACtrack Skyn to real-time breathalyzer measurements collected in the laboratory and in the field. RESULTS: The newest version of the Skyn device demonstrates a substantially lower error rate than older hand-assembled prototypes (0% to 7% vs. 29% to 53%, respectively). On average, real-time estimates of BrAC yielded by these transdermal sensors are within 0.007 of true BAC readings in the laboratory context and within 0.019 of true BrAC readings in the field. In both contexts, the distance between true and estimated BrAC was larger when only alcohol episodes were examined (laboratory = 0.017; field = 0.041). Finally, results of power-law-curve projections indicate that, given their accuracy, transdermal BrAC estimates in real-world contexts have the potential to improve markedly (>25%) with adequately sized datasets for model training. CONCLUSION: Findings from this study indicate that the latest version of the transdermal wrist sensor holds promise for the accurate assessment of alcohol consumption in field contexts. A great deal of additional work is needed to provide a full picture of the utility of these devices, including research with large participant samples in field contexts.

Automated video interview personality assessments: Reliability, validity, and generalizability investigations.

Organizations are increasingly adopting automated video interviews (AVIs) to screen job applicants despite a paucity of research on their reliability, validity, and generalizability. In this study, we address this gap by developing AVIs that use verbal, paraverbal, and nonverbal behaviors extracted from video interviews to assess Big Five personality traits. We developed and validated machine learning models within (using nested cross-validation) and across three separate samples of mock video interviews (total N = 1,073). Also, we examined their test-retest reliability in a fourth sample (N = 99). In general, we found that the AVI personality assessments exhibited stronger evidence of validity when they were trained on interviewer-reports rather than self-reports. When cross-validated in the other samples, AVI personality assessments trained on interviewer-reports had mixed evidence of reliability, exhibited consistent convergent and discriminant relations, used predictors that appear to be conceptually relevant to the focal traits, and predicted academic outcomes. On the other hand, there was little evidence of reliability or validity for the AVIs trained on self-reports. We discuss the implications for future work on AVIs and personality theory, and provide practical recommendations for the vendors marketing such approaches and organizations considering adopting them. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Alcohol narrows physical distance between strangers.

Pandemic management is likely to represent a global reality for years to come, but the roadmap for how to approach pandemic restrictions is as yet unclear. Of the restrictions enacted during COVID-19, among the more controversial surround alcohol. Like many infectious diseases, the principal mode of transmission for COVID-19 is direct respiration of droplets emitted during close social contact, and health officials warn that alcohol consumption may lead to decreased adherence to physical distancing guidelines. Governing bodies have acted to close bars before restaurants and have also specifically restricted alcohol sales, while at the same time those in the nightlife industry have labeled such actions unfounded and discriminatory. Complicating such debates is the lack of evidence on alcohol's effects on physical distance. In the current study we employed a randomized alcohol-administration design paired with computer-vision measures, analyzing over 20,000 proximity readings derived from video to examine the effect of alcohol consumption on physical distance during social interaction. Results indicated that alcohol caused individuals to draw significantly closer to an unfamiliar interaction partner during social exchange, reducing physical proximity at a rate with potentially important implications for public health. In contrast, alcohol had no effect on physical distance with a familiar interaction partner. Findings suggest that alcohol might act to overcome a natural caution people feel towards strangers and thus promote virus transmission between previously unconnected social groups.

A new generation of transdermal alcohol biosensing technology: practical applications, machine -learning analytics and questions for future research.

The use of transdermal alcohol monitors has burgeoned in recent years, now encompassing hundreds of thousands of individuals globally. A new generation of sensors promises to expand the range of applications for transdermal technology exponentially, and advances in machine-learning modeling approaches offer new methods for translating the data produced by transdermal devices. This article provides (1) a review of transdermal sensor research conducted to date, including an analysis of methodological features of past studies potentially key in driving reported sensor performance; (2) updates on methodological developments likely to be transformative for the field of transdermal sensing, including the development of new-generation sensors featuring smartphone integration and rapid sampling capabilities as well as developments in machine-learning analytics suited to data produced by these novel sensors and; (3) an analysis of the expanded range of applications for this new generation of sensor, together with corresponding requirements for sensor accuracy and temporal specificity. We also note questions as yet unanswered and key directions for future research.

Identifying Supportive Student Factors for Mindset Interventions: A Two-model Machine Learning Approach.

Growth mindset interventions foster students' beliefs that their abilities can grow through effort and appropriate strategies. However, not every student benefits from such interventions - yet research identifying which student factors support growth mindset interventions is sparse. In this study, we utilized machine learning methods to predict growth mindset effectiveness in a nationwide experiment in the U.S. with over 10,000 students. These methods enable analysis of arbitrarily-complex interactions between combinations of student-level predictor variables and intervention outcome, defined as the improvement in grade point average (GPA) during the transition to high school. We utilized two separate machine learning models: one to control for complex relationships between 51 student-level predictors and GPA, and one to predict the change in GPA due to the intervention. We analyzed the trained models to discover which features influenced model predictions most, finding that prior academic achievement, blocked navigations (attempting to navigate through the intervention software too quickly), self-reported reasons for learning, and race/ethnicity were the most important predictors in the model for predicting intervention effectiveness. As in previous research, we found that the intervention was most effective for students with prior low academic achievement. Unique to this study, we found that blocked navigations predicted an intervention effect as low as 0.185 GPA points (on a 0-4 scale) less than the mean. This was a notable negative prediction given that the mean intervention effect in our sample was just 0.026 GPA points, though few students (4.4%) experienced a substantial number of blocked navigation events. We also found that some minoritized students were predicted to benefit less (or even not at all) from the intervention. Our findings have implications for the design of computer-administered growth mindset interventions, especially in relation to students who experience procedural difficulties completing the intervention.

Using machine learning for real-time BAC estimation from a new-generation transdermal biosensor in the laboratory.

BACKGROUND: Transdermal biosensors offer a noninvasive, low-cost technology for the assessment of alcohol consumption with broad potential applications in addiction science. Older-generation transdermal devices feature bulky designs and sparse sampling intervals, limiting potential applications for transdermal technology. Recently a new-generation of transdermal device has become available, featuring smartphone connectivity, compact designs, and rapid sampling. Here we present initial laboratory research examining the validity of a new-generation transdermal sensor prototype. METHODS: Participants were young drinkers administered alcohol (target BAC = .08 %) or no-alcohol in the laboratory. Participants wore transdermal sensors while providing repeated breathalyzer (BrAC) readings. We assessed the association between BrAC (measured BrAC for a specific time point) and eBrAC (BrAC estimated based only on transdermal readings collected in the immediately preceding time interval). Extra-Trees machine learning algorithms, incorporating transdermal time series features as predictors, were used to create eBrAC. RESULTS: Failure rates for the new-generation prototype sensor were high (16 %-34 %). Among participants with useable new-generation sensor data, models demonstrated strong capabilities for separating drinking from non-drinking episodes, and significant (moderate) ability to differentiate BrAC levels within intoxicated participants. Differences between eBrAC and BrAC were 60 % higher for models based on data from old-generation vs new-generation devices. Model comparisons indicated that both time series analysis and machine learning contributed significantly to final model accuracy. CONCLUSIONS: Results provide favorable preliminary evidence for the accuracy of real-time BAC estimates from a new-generation sensor. Future research featuring variable alcohol doses and real-world contexts will be required to further validate these devices.

Automatically classifying the evidence type of drug-drug interaction research papers as a step toward computer supported evidence curation.

A longstanding issue with knowledge bases that discuss drug-drug interactions (DDIs) is that they are inconsistent with one another. Computerized support might help experts be more objective in assessing DDI evidence. A requirement for such systems is accurate automatic classification of evidence types. In this pilot study, we developed a hierarchical classifier to classify clinical DDI studies into formally defined evidence types. The area under the ROC curve for sub-classifiers in the ensemble ranged from 0.78 to 0.87. The entire system achieved an F1 of 0.83 and 0.63 on two held-out datasets, the latter consisting focused on completely novel drugs from what the system was trained on. The results suggest that it is feasible to accurately automate the classification of a sub-set of DDI evidence types and that the hierarchical approach shows promise. Future work will test more advanced feature engineering techniques while expanding the system to classify a more complex set of evidence types.