R. Turrisi’s research while affiliated with Pennsylvania State University and other places

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.

Objective: Transdermal alcohol concentration (TAC) sensors capture aspects of drinking events that self-reports cannot. The multidimensional nature of TAC data allows novel classification of drinking days and identification of associated behavioral and contextual risks. We used multilevel latent profile analysis (MLPA) to create day-level profiles of TAC features and test their associations with (a) daily behaviors and contexts and (b) risk for alcohol use disorders at baseline. Method: Two hundred twenty-two regularly heavy-drinking young adults (Mage = 22.3) completed the Alcohol Use Disorders Identification Test (AUDIT) at baseline and then responded to mobile phone surveys and wore TAC sensors for six consecutive days. MLPA identified day-level profiles using four TAC features (peak, rise rate, fall rate, and duration). TAC profiles were tested as correlates of daily drinking behaviors, contexts, and baseline AUDIT. Results: Four profiles emerged: (a) high-fast (8.5% of days), (b) moderate-fast (12.8%), (c) low-slow (20.4%), and (d) little-to-no drinking days (58.2%). Profiles differed in the odds of risky drinking behaviors and contexts. The highest risk occurred on high-fast days, followed by moderate-fast, low-slow, and little-to-no drinking days. Higher baseline AUDIT predicted higher odds of high-fast and moderate-fast days. Conclusions: Days with high and fast intoxication are reflective of high-risk drinking behaviors and were most frequent among those at risk for alcohol use disorders. TAC research using MLPA may offer novel and important insights to intervention efforts.

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.

Objective: Subjective intoxication (SI) when drinking may serve as an internal barometer of whether to continue drinking or engage in potentially unsafe behavior. Mobile assessments offer the potential to use SI as a prospective risk indicator during drinking episodes; little evidence exists for the validity of real-time SI measures. We test the correspondence of SI with estimated blood alcohol concentration and transdermal alcohol concentration (TAC) in young adults’ natural settings. We provide a novel test of whether SI features (peak and mean SI) uniquely predict consequences adjusting for alcohol concentration. Method: Two hundred twenty-two heavy-drinking young adults (Mage = 22.3, 64% female, 79% non-Hispanic White, 84% undergraduates) participated in a 6-day study that used ecological momentary assessment of drinking and TAC sensors. SI was assessed every 30 min during drinking episodes. Multilevel modeling was used to test hypotheses. Results: Momentary SI and estimated blood alcohol concentration had moderate associations at the moment and day levels (standardized βs = 0.5–0.6); SI was moderately associated with TAC at the day level (βs = 0.5). Associations between SI and alcohol concentration varied widely between persons and across days. Day-level SI features predicted consequences when adjusting for alcohol concentration (incidence rate ratios, IRRs = 1.29–1.70). Conclusions: Our two-item SI measure shows evidence of validity in real-world settings with heavy-drinking young adults. SI was significantly correlated with alcohol concentration and was a unique predictor of consequences. The strength of these associations varied greatly across persons and days. Real-time SI measurement may be useful in preventive interventions, but continued research is needed into when and for whom momentary SI is most predictive of risk.

Objective: Utilize a dual-process decision-making model to examine the longitudinal associations between alcohol-induced blackouts (blackouts) and alcohol use disorder (AUD) risk symptoms among college student drinkers. Method: Undergraduate drinkers (N = 2,024; 56% female; 87% White; 5% Hispanic) at a large northeastern university completed online surveys each semester during their first (T1, T2), second (T3, T4), third (T5, T6), and fourth (T7, T8) years of college (87% retention across the study). Path analyses were examined testing the longitudinal associations between T1 willingness to experience a blackout, T1 intentions to avoid a blackout, T2-T8 drinking, T2-T8 blackouts, and T8 AUD risk symptoms. Hypotheses 1-2 tested the associations between T1 willingness, T1 intentions, T2-T8 drinking, and T2-T8 blackouts. Hypothesis 3 tested the associations between T2-T8 drinking, T2-T8 blackouts, and T8 AUD risk symptoms. Results: Students experienced an average of 8 (SD = 8) blackouts during college. Approximately 1,457 (88.8%) of participants reported experiencing 1 of 8 AUD risk symptoms. T1 willingness was positively associated with T2-T8 blackouts. T2-T8 drinking and T2-T8 blackouts were positively associated with T8 AUD risk symptoms. T1 willingness significantly indirectly impacted T8 AUD risk symptoms through its association with T2-T8 blackouts. Conclusions: Results estimated that, on average, college student drinkers experienced 8 blackouts across 4 years of college and 88% of participants reported experiencing at least one symptom of AUD in the last semester of college. Willingness to experience a blackout influenced students' AUD risk symptoms through the number of blackouts they experienced throughout college.

Background Alcohol‐induced blackouts (AIBs) are experienced frequently by college student drinkers and are more likely to occur on days with high‐intensity drinking (HID; 8+ for females/10+ for males) than non‐HID days. Research suggests that AIBs are associated with experiencing other alcohol‐related consequences (ARCs), including more serious ARCs (SARCs; e.g., legal and sexual consequences), but we do not know whether individuals experience more ARCs and more SARCs on occasions when they black out than when they do not black out. This study examines the associations between AIBs and the total number of both ARCs and SARCs. Methods Students (N = 462, 51.7% female, 87.7% White, Mage = 20.1) were assessed across 6 weekends via e‐surveys (80%–97% response rate). Multilevel models were used to test for main effects, controlling for drinking (HID or estimated blood alcohol concentration; eBAC) and sex. Results Drinking days when an AIB was experienced were associated with more total ARCs (b = 3.54, 95% CI: 3.10, 3.99) and more SARCs (b = 0.77, 95% CI: 0.60, 0.95) per day than non‐AIB days. The more frequently a person experienced an AIB, the more total ARCs (b = 5.33, 95% CI: 4.40, 6.25) and SARCs (1.05, 95% CI: 0.80, 1.30) they reported on average. Conclusion Alcohol‐induced blackout days were associated with higher levels of harm than non‐AIB days, even at the same levels of drinking. Interventions that focus on reducing the occurrence of AIBs and factors that contribute to them, in addition to reducing alcohol consumption, may help reduce total harm associated with drinking among college students.

Objective: To identify factors (manner of drinking, combined alcohol and other substance use, physiology) that are associated with alcohol-induced blackouts (AIBs) over and above estimated blood alcohol concentration (eBAC). Methods: Students (N = 462, 51.7 % female, 87.7 % White, Mage = 20.1) were assessed across 6 weekends via e-surveys (80-97 % response rate). eBAC was calculated using standard number of drinks, drinking duration, sex, and weight. Three-level multilevel models (days, weeks, persons) were conducted to test for main effects, controlling for eBAC. Results: Protective behavioral strategies (PBS) were associated with decreased odds of AIBs on the daily (OR = 0.64, 95 % CI: 0.53, 0.77), weekly (OR = 0.84, 95 % CI: 0.72, 0.98), and person-levels (OR = 0.62, 95 % CI: 0.51, 0.74). Combined cannabis with alcohol was associated with increased odds of AIBs on the weekly (OR = 2.13, 95 % CI 1.13, 4.07) and person-levels (OR = 3.56, 95 % CI 1.60, 7.93). People who more frequently played drinking games (OR = 1.41, 95 % CI: 1.12, 1.77), pregamed (OR = 1.55, 95 % CI: 1.19, 2.03), and showed higher tolerance (OR = 1.22, 95 % CI: 1.08, 1.37) showed increased risk of AIBs, over and above eBAC levels. Conclusion: We identified a number of daily-, weekly-, and person-level factors that uniquely contribute to the prediction of AIBs even at equivalent eBACs. Many of these factors were behavioral, suggesting that they may serve as malleable prevention targets for AIBs in college student drinkers.

This study is a formative assessment of REAL Parenting (RP): a brief, digital intervention for parents of high school students that encourages parent-teen communication about alcohol and, in turn, aims to prevent teen alcohol use. The aims of this study were to describe engagement with, and acceptability and usability of RP; and to explore the relationship of these measures with each other and with short-term outcomes. Participants were 160 parents randomly assigned to the treatment group who received RP as part of a randomized pilot trial (Mage = 45.43[SD = 7.26], 59.3% female, 56% White, 19% Hispanic). App-based program analytics captured real-time engagement with RP. Parents completed self-report measures of acceptability, usability, perceived communication effectiveness, perceived self-efficacy to communicate, and frequency of communication post-intervention. Descriptive statistics were calculated to describe engagement, acceptability and usability, and zero-order correlations were calculated to examine associations between these and self-report variables. About 75% (n = 118) of parents accessed the intervention and two-thirds (n = 110) accessed at least one module. Self-report ratings of acceptability and usability were neutral to positive, and mothers liked RP more than fathers. Self-report, but not program analytic indicators were associated with short-term outcomes. Findings suggest that, with little incentive, most parents will access an app focused on parent-teen communication about alcohol. While parent feedback was positive, it also highlighted areas for improvement with app content and design. Correlations suggest that analytic metrics of engagement are useful to discern who is and is not using interventions, and self-report measures are important for understanding pathways by which interventions are associated with short-term outcomes.