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Profiles of Transdermal Alcohol Concentration and Their Prediction of Negative and Positive Alcohol-Related Consequences in Young Adults’ Natural Settings

American Psychological Association
Psychology of Addictive Behaviors
Authors:
Veronica Richards at University of Oklahoma Health Sciences Center
Veronica Richards
Kimberly A. Mallett
Kimberly A. Mallett
Kimberly A. Mallett
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Robert J. Turrisi
Robert J. Turrisi
Robert J. Turrisi
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Shannon D Glenn at Pennsylvania State University
Shannon D Glenn
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Abstract

Objective: Transdermal alcohol concentration (TAC) sensors provide a multidimensional characterization of drinking events that self-reports cannot. These profiles may differ in their associated day-level alcohol-related consequences, but no research has tested this. We address this using multilevel latent profile analysis. Method: Two hundred twenty-two young adults who regularly engage in heavy drinking (Mage = 22.3, 64% female, 79% non-Hispanic White) responded to surveys and wore TAC sensors for 6 consecutive days. We tested whether four previously identified TAC profiles: (1) high-fast (8.5% of days), (2) moderate-fast (12.8%), (3) low-slow (20.4%), and (4) little-to-no-drinking days (58.2%) differed in numbers of negative and positive consequences and in the odds that both consequence types occurred on the same day. Results: High-fast (incident rate ratio [IRRlow-slow] = 6.18; IRRlittle-to-no-drinking = 9.47) and moderate-fast (IRRlow-slow = 3.71; IRRlittle-to-no-drinking = 5.68) days contained more negative consequences compared to low-slow and little-to-no-drinking days. High-fast (IRR = 2.05), moderate-fast (IRR = 1.88), and low-slow (IRR = 1.43) days contained more positive consequences than little-to-no-drinking days. The odds of having only positive consequences were highest on low-slow, χ²(3) = 9.10, p < .05, days but the odds of experiencing both consequence types increased on moderate-fast and high-fast days, χ²(3) = 39.63, p < .001. Conclusions: Compared to little-to-no-drinking days, TAC profiles indicative of drinking (high-fast, moderate-fast, and low-slow) contained more negative and positive consequences. However, the odds of experiencing only positive consequences were highest among low-slow days and decreased on moderate-fast and high-fast days as the odds of negative consequences rose. These findings provide novel evidence reinforcing harm reduction approaches that seek to maximize positives and minimize negatives of alcohol consumption through emphasis on slow-paced, low-volume drinking.
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Profiles of Transdermal Alcohol Concentration and Their Prediction of
Negative and Positive Alcohol-Related Consequences in Young Adults’
Natural Settings
Veronica L. Richards
1, 2
, Kimberly A. Mallett
3
, Robert J. Turrisi
3, 4
,
Shannon D. Glenn
3, 4
, and Michael A. Russell
3, 4
1
TSET Health Promotion Research Center, Stephenson Cancer Center, The University of Oklahoma Health Sciences
2
Department of Health Promotion Sciences, The University of Oklahoma Health Sciences
3
Edna Bennett Pierce Prevention Research Center, College of Health and Human Development, The Pennsylvania State University
4
Department of Biobehavioral Health, The Pennsylvania State University
Objective: Transdermal alcohol concentration (TAC) sensors provide a multidimensional characterization
of drinking events that self-reports cannot. These profiles may differ in their associated day-level alcohol-
related consequences, but no research has tested this. We address this using multilevel latent profile analysis.
Method: Two hundred twenty-two young adults who regularly engage in heavy drinking (M
age
=22.3, 64%
female, 79% non-Hispanic White) responded to surveys and wore TAC sensors for 6 consecutive days.
We tested whether four previously identified TAC profiles: (1) high-fast (8.5% of days), (2) moderate-
fast (12.8%), (3) low-slow (20.4%), and (4) little-to-no-drinking days (58.2%) differed in numbers
of negative and positive consequences and in the odds that both consequence types occurred on the
same day. Results: High-fast (incident rate ratio [IRR
low-slow
]=6.18; IRR
little-to-no-drinking
=9.47) and
moderate-fast (IRR
low-slow
=3.71; IRR
little-to-no-drinking
=5.68) days contained more negative consequences
compared to low-slow and little-to-no-drinking days. High-fast (IRR =2.05), moderate-fast (IRR =1.88),
and low-slow (IRR =1.43) days contained more positive consequences than little-to-no-drinking days. The
odds of having only positive consequences were highest on low-slow, χ
2
(3) =9.10, p<.05, days but the
odds of experiencing both consequence types increased on moderate-fast and high-fast days, χ
2
(3) =39.63,
p<.001. Conclusions: Compared to little-to-no-drinking days, TAC profiles indicative of drinking (high-
fast, moderate-fast, and low-slow) contained more negative and positive consequences. However, the odds
of experiencing only positive consequences were highest among low-slow days and decreased on moderate-
fast and high-fast days as the odds of negative consequences rose. These findings provide novel evidence
reinforcing harm reduction approaches that seek to maximize positives and minimize negatives of alcohol
consumption through emphasis on slow-paced, low-volume drinking.
Public Health Significance Statement
We used profiles of objective intoxication (transdermal alcohol concentration; TAC) from a wearable
sensor to predict alcohol-related consequences among 222 regularly heavy drinking young adults
(average age =22.3). Our results showed that high-speed, high-peak TAC days and high-speed,
moderate-peak TAC days were associated with the most risk (e.g., more days with negative
consequences and fewer days with only positive consequences). These findings suggest that these
days are clear targets for harm reduction interventions.
Keywords: young adult drinking, alcohol-related consequences, transdermal alcohol concentration,
multilevel latent profile analysis
Supplemental materials: https://doi.org/10.1037/adb0001054.supp
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
This article was published Online First January 13, 2025.
Christian Hendershot served as action editor.
Veronica L. Richards https://orcid.org/0000-0002-1391-0607
This research was funded in part by pilot mentoring and professional
development awards through P50DA039838 awarded to Michael A. Russell
(National Institute on Drug Abuse, Principal Investigator: Collins) and the
Social Science Research Institute at Penn State, in addition to departmental
funds awarded to Michael A. Russell. Veronica L. Richards and Shannon D.
Glenn were supported by the National Institutes of Health (T32 DA017629;
multiple principal investigators: J. Maggs and S. Lanza). The content is
solely the responsibility of the authors and does not necessarily represent the
official views of the National Institutes of Health.
Veronica L. Richards played a lead role in conceptualization, formal
analysis, writing–original draft, and writing–review and editing and an
equal role in methodology. Kimberly A. Mallett played a supporting role
in writing–original draft and writing–review and editing. Robert J. Turrisi
played a supporting role in conceptualization and writing–review and
editing. Shannon D. Glenn played a supporting role in writing–review and
continued
Psychology of Addictive Behaviors
© 2025 American Psychological Association 2025, Vol. 39, No. 2, 163–172
ISSN: 0893-164X https://doi.org/10.1037/adb0001054
163
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Transdermal alcohol concentration features predict alcohol‐induced blackouts in college students
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Background Alcohol‐induced blackouts (AIBs) are common in college students. Individuals with AIBs also experience acute and chronic alcohol‐related consequences. Research suggests that how students drink is an important predictor of AIBs. We used transdermal alcohol concentration (TAC) sensors to measure biomarkers of increasing alcohol intoxication (rise rate, peak, and rise duration) in a sample of college students. We hypothesized that the TAC biomarkers would be positively associated with AIBs. Methods Students were eligible to participate if they were aged 18–22 years, in their second or third year of college, reported drinking 4+ drinks on a typical Friday or Saturday, experienced ≥1 AIB in the past semester, owned an iPhone, and were willing to wear a sensor for 3 days each weekend. Students (N = 79, 55.7% female, 86.1% White, Mage = 20.1) wore TAC sensors and completed daily diaries over four consecutive weekends (89.9% completion rate). AIBs were assessed using the Alcohol‐Induced Blackout Measure‐2. Logistic multilevel models were conducted to test for main effects. Results Days with faster TAC rise rates (OR = 2.69, 95% CI: 1.56, 5.90), higher peak TACs (OR = 2.93, 95% CI: 1.64, 7.11), and longer rise TAC durations (OR = 4.16, 95% CI: 2.08, 10.62) were associated with greater odds of experiencing an AIB. Conclusions In a sample of "risky" drinking college students, three TAC drinking features identified as being related to rising intoxication independently predicted the risk for daily AIBs. Our findings suggest that considering how an individual drinks (assessed using TAC biomarkers), rather than quantity alone, is important for assessing risk and has implications for efforts to reduce risk. Not only is speed of intoxication important for predicting AIBs, but the height of the peak intoxication and the time spent reaching the peak are important predictors, each with different implications for prevention.
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Article
Full-text available
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Baseline Protective Behavioral Strategy Use Predicts More Moderate Transdermal Alcohol Concentration Dynamics and Fewer Negative Consequences of Drinking in Young Adults’ Natural Settings
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Article
Full-text available
  • Jan 2022
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In this article we provide an overview of existing approaches for relating latent class membership to external variables of interest. We extend on the work of Nylund-Gibson et al. (Structural Equation Modeling: A Multidisciplinary Journal, 2019, 26, 967), who summarize models with distal outcomes by providing an overview of most recommended modeling options for models with covariates and larger models with multiple latent variables as well. We exemplify the modeling approaches using data from the General Social Survey for a model with a distal outcome where underlying model assumptions are violated, and a model with multiple latent variables. We discuss software availability and provide example syntax for the real data examples in Latent GOLD.
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Assessment of Alcohol Use in the Natural Environment
Article
Full-text available
  • Mar 2019
The current article critically reviews 3 methodological options for assessing drinking episodes in the natural environment. Ecological momentary assessment (EMA) typically involves using mobile devices to collect self‐report data from participants in daily life. This technique is now widely used in alcohol research, but investigators have implemented diverse assessment strategies. This article focuses on “high‐resolution” EMA protocols that oversample experiences and behaviors within individual drinking episodes. A number of approaches have been used to accomplish this, including using signaled follow‐ups tied to drinking initiation, asking participants to log entries before and after individual drinks or drinking episodes, and delivering frequent signaled assessments during periods of the day when alcohol use is most common. Transdermal alcohol sensors (TAS) are devices that are worn continuously and are capable of detecting alcohol eliminated through the skin. These methods are appealing because they do not rely upon drinkers’ self‐report. Studies using TAS have been appearing with greater frequency over the past several years. New methods are making the use of TAS more tractable by permitting back‐translation of transdermal alcohol concentration data to more familiar estimates of blood alcohol concentration or breath alcohol concentration. However, the current generation of devices can have problems with missing data and tend to be relatively insensitive to low‐level drinking. An emerging area of research investigates the possibility of using mobile device data and machine learning to passively detect the user's drinking, with promising early findings. EMA, TAS, and sensor‐based approaches are all valid, and tend to produce convergent information when used in conjunction with one another. Each has a unique profile of advantages, disadvantages, and threats to validity. Therefore, the nature of the underlying research question must dictate the method(s) investigators select.
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Characterising patterns of alcohol use among heavy drinkers: A cluster analysis utilising alcohol biosensor data
Article
  • May 2021
Introduction Previous research has predominately relied on person‐level or single characteristics of drinking episodes to characterise patterns of drinking that may confer risk. This research often relies on self‐report measures. Advancements in wearable alcohol biosensors provide a multi‐faceted objective measure of drinking. The current study aimed to characterise drinking episodes using data derived from a wearable alcohol biosensor. Methods Participants (n = 45) were adult heavy drinkers who wore the Secure Continuous Remote Alcohol Monitoring (SCRAM) bracelet and reported on their drinking behaviours. Cluster analysis was used to evaluate unique combinations of alcohol episode characteristics. Associations between clusters and self‐reported person and event‐level factors were also examined in univariable and multivariable models. Results Results suggested three unique clusters: Cluster 1 (most common, slowest rate of rise to and decline from peak), Cluster 2 (highest peak transdermal alcohol concentration and area under the curve) and Cluster 3 (fastest rate of decline from peak). Univariable analyses distinguished Cluster 1 as having fewer self‐reported drinks and fewer episodes that occurred on weekends relative to Cluster 2. The effect for number of drinks remained in multivariable analyses. Discussion and Conclusions This is the first study to characterise drinking patterns at the event‐level using objective data. Results suggest that it is possible to distinguish drinking episodes based on several characteristics derived from wearable alcohol biosensors. This examination lays the groundwork for future studies to characterise patterns of drinking and their association with consequences of drinking behaviour.
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How much and how fast: Alcohol consumption patterns, drinking-episode affect, and next-day consequences in the daily life of underage heavy drinkers
Article
  • Nov 2020
  • DRUG ALCOHOL DEPEN
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Processing Transdermal Alcohol Concentration (TAC) data to Detect Low-Level Drinking
Article
  • Sep 2018
  • ALCOHOL
Background: Several studies have objectively quantified drinking through the use of Alcohol Monitoring System's (AMS) transdermal alcohol concentration (TAC) device known as SCRAM CAM. Criteria that AMS uses to detect drinking are known to be conservative and only reliably detect heavy drinking equivalent to 5 or more standard drinks. Our group has developed Research Rules used to process TAC data in a manner that will detect low-level and moderate drinking even though it is below the AMS criteria for detection. Methods: 16 male and 14 female paid research volunteers wore TAC monitors for 28 days in their natural environment and responded daily to text message prompts to self-report yesterday's drinking. Current analyses describe the Research Rules that we developed and how use of those rules impact the detection of self-reported drinking treated as a Gold Standard in Sensitivity/Specificity analysis. Results: We observed 606 occurrences of positive TAC events over a total of 867 days and processed the TAC data to retain 345 as possible drinking events even though AMS criteria confirmed drinking for only 163 of these events. The kinds of TAC events removed or retained by our rules are illustrated as are cases of low and moderate drinking days that were detected by our rules but not by the conservative AMS criteria. AMS Confirmed TAC events have a high specificity (99.8%) to detect primarily heavy drinking, but have a poor sensitivity to detect lower level drinking and a poor specificity as an indicator of alcohol abstinence. In contrast, our Research Rules detected 100% of TAC events detected by AMS but also detected 31% of the lower-level drinking events not detected by AMS with 91% specificity. Conclusions: Reliance upon the AMS criteria for alcohol detection affords a high specificity for detection of heavy drinking but is a poor indicator of abstinence rates. In contrast use of our Research Rules provides more sensitive means to quantify either any drinking or low-moderate levels of drinking while still maintaining good specificity.
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