BackgroundThe development of a transdermal alcohol biosensor could represent a tremendous advance toward curbing problematic drinking. But several factors limit the usefulness of extant transdermal technology, including relatively lengthy delays between blood alcohol concentration (BAC) and transdermal alcohol concentration (TAC), as well as the large/bulky designs of currently available transdermal sensors (e.g., ankle monitors). The current research examined the lag time between BAC and TAC using a prototype of BACtrack Skyn—a new‐generation wrist‐worn transdermal sensor featuring a compact design and smartphone integration.MethodsParticipants (N = 30) received either a dose of alcohol (target BAC 0.08%) or a nonalcoholic beverage in the laboratory while wearing both the AMS SCRAM ankle monitor and a Skyn prototype. Participants were monitored in the laboratory until breath alcohol concentration (BrAC) dropped below 0.025%.ResultsDevice failure rates for Skyn prototypes were relatively high (18 to 38%) compared with nonprototype SCRAM devices (2%). Among participants with usable data, both Skyn‐ and SCRAM‐measured TAC showed strong correlations with BrAC, and both Skyn and SCRAM devices detected alcohol within 30 minutes of first alcohol administration. Skyn‐measured TAC peaked over 1 hour earlier than SCRAM‐measured TAC (54 versus 120 minutes after peak BrAC, respectively), and time‐series models suggested that, on average across all measured portions of the BrAC curve, Skyn TAC lagged behind BrAC by 24 minutes, whereas SCRAM TAC lagged behind BrAC by 69 minutes—all differences statistically significant at p < 0.001.ConclusionsResults provide preliminary evidence for the validity of a new‐generation wrist‐worn transdermal sensor under controlled laboratory conditions and further suggest favorable properties of this sensor as they pertain to the latency of transdermal alcohol detection. The prototype version of Skyn employed here displayed a higher failure rate compared with SCRAM, and, in future, more reliable and robust Skyn prototypes will be required suitable to field testing across diverse environmental conditions.

Substance use has long been associated with close relationship distress. While the direction of influence for this association has not been established, it has often been assumed that substance use is the causal agent and that close relationship distress is the effect. But research seeking to establish temporal precedence in this link has produced mixed findings. Further, theoretical models of substance use and close relationship processes present the plausibility of the inverse pathway—that insecure close relationships may serve as a vulnerability factor for the development of later substance problems. The current review applies an attachment-theoretical framework to the association between close social bonds and substance use and substance-related problems. Targeting longitudinal studies of attachment and substance use, we examined 665 effect sizes drawn from 34 samples (total N=56,721) spanning time frames ranging from 1 month to 20 years (M=3.8 years). Results revealed a significant prospective correlation between earlier attachment and later substance use (r =−.11, 95%CI=−.14 to −0.08). Further, cross-lagged coefficients were calculated which parsed auto-regressive effects, indicating that lower attachment security temporally preceded increases in substance use (r=−.05, 95%CI=−.06 to −.04). Analyses further indicated that the pathway from earlier attachment to later substance use was significantly stronger than that from earlier substance use to later attachment. Results also revealed several moderators of the attachment-substance use link. These findings suggest that insecure attachment may be a vulnerability factor for substance use, and indicate close relationship quality as a promising line of inquiry in research on substance use disorder risk.

Regular alcohol consumption in unfamiliar social settings has been linked to problematic drinking. A large body of indirect evidence has accumulated to suggest that alcohol's rewarding emotional effects-both negative-mood relieving and positive-mood enhancing-will be magnified when alcohol is consumed within unfamiliar versus familiar social contexts. But empirical research has never directly examined links between contextual familiarity and alcohol reward. In the current study, we mobilized novel ambulatory technology to examine the effect of social familiarity on alcohol reward in everyday drinking contexts while also examining how alcohol reward observed in these field contexts corresponds to reward observed in the laboratory. Heavy social drinking participants (N = 48, 50% male) engaged in an intensive week of ambulatory assessment. Participants wore transdermal alcohol sensors while they reported on their mood and took photographs of their social contexts in response to random prompts. Participants also attended 2 laboratory beverage-administration sessions, during which their emotional responses were assessed and transdermal sensors were calibrated to estimate breathalyzer readings (eBrACs). Results indicated a significant interaction between social familiarity and alcohol episode in everyday drinking settings, with alcohol enhancing mood to a greater extent in relatively unfamiliar versus familiar social contexts. Findings also indicated that drinking in relatively unfamiliar social settings was associated with higher eBrACs. Finally, results indicated a correspondence between some mood effects of alcohol experienced inside and outside the laboratory. This study presents a novel methodology for examining alcohol reward and indicates social familiarity as a promising direction for research seeking to explain problematic drinking. (PsycINFO Database Record

We estimate the distribution of random parameters in a distributed parameter model with unbounded input and output for the transdermal transport of ethanol in humans. The model takes the form of a diffusion equation with the input being the blood alcohol concentration and the output being the transdermal alcohol concentration. Our approach is based on the idea of reformulating the underlying dynamical system in such a way that the random parameters are now treated as additional space variables. When the distribution to be estimated is assumed to be defined in terms of a joint density, estimating the distribution is equivalent to estimating the diffusivity in a multi-dimensional diffusion equation and thus well-established finite dimensional approximation schemes, functional analytic based convergence arguments, optimization techniques, and computational methods may all be employed. We use our technique to estimate a bivariate normal distribution based on data for multiple drinking episodes from a single subject.

The distribution of random parameters in, and the input signal to, a distributed parameter model with unbounded input and output operators for the transdermal transport of ethanol are estimated. The model takes the form of a diffusion equation with the input, which is on the boundary of the domain, being the blood or breath alcohol concentration (BAC/BrAC), and the output, also on the boundary, arXiv:1807.05088v1 [math.OC] 13 Jul 2018 being the transdermal alcohol concentration (TAC). Our approach is based on the reformulation of the underlying dynamical system in such a way that the random parameters are treated as additional spatial variables. When the distribution to be estimated is assumed to be defined in terms of a joint density, estimating the distribution is equivalent to estimating a functional diffusivity in a multi-dimensional diffusion equation. The resulting system is referred to as a population model, and well-established finite dimensional approximation schemes, functional analytic based convergence arguments, optimization techniques, and computational methods can be used to fit it to population data and to analyze the resulting fit. Once the forward population model has been identified or trained based on a sample from the population, the resulting distribution can then be used to deconvolve the BAC/BrAC input signal from the biosensor observed TAC output signal formulated as either a quadratic programming or linear quadratic tracking problem. In addition, our approach allows for the direct computation of corresponding credible bands without simulation. We use our technique to estimate bivariate normal distributions and deconvolve BAC/BrAC from TAC based on data from a population that consists of multiple drinking episodes from a single subject and a population consisting of single drinking episodes from multiple subjects.