Background: Transdermal biosensors offer a noninvasive, low-cost technology for the assessment of alcohol consumption with broad potential applications in addiction science. Oldergeneration 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.

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Objective: Emotional distress has been posited as a key underlying mechanism in the development and maintenance of substance use disorder (SUD), and patients seeking SUD treatment are often experiencing high levels of negative emotion and/or low levels of positive emotion. But the extent to which SUD interventions impact emotional outcomes among general SUD populations is yet unquantified. The current meta-analysis aims to fill this gap. Method: A total of 11,754 records were screened for randomized, controlled trials examining the effect of behavioral SUD interventions on emotion outcomes. Our search yielded a total of 138 effect sizes calculated based on data from 5,146 individuals enrolled in 30 independent clinical trials. Random-effects meta-analysis was used to calculate pooled effect sizes, and metaregression analyses examined study-level moderators (e.g., intervention type). Results: Findings indicated a small but significant effect of SUD interventions on emotion outcomes, d = 0.157, 95% CI [0.052, 0.262] (k = 30). The effect size for negative emotion was nominally bigger, d = 0.162, 95% CI [0.056, 0.269] (k = 30), whereas the effect for positive emotion did not reach statistical significance, d = 0.062, 95% CI [−0.089, 0.213] (k = 7). Studies featuring SUD interventions designed to specifically target emotions (i.e., affect-regulation, mindfulness-based treatments) produced larger reductions in negative emotion compared with studies featuring interventions that did not contain specific emotion modules (e.g., contingency management). Conclusions: Findings suggest that SUD interventions—especially mindfulness-based and affect-regulation treatments—indeed significantly reduce negative emotion, although relatively small effect sizes indicate potential room for improvement. Conclusions regarding positive emotion should be considered preliminary because of the limited numbers of samples assessing these outcomes.

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