Effects of nicotine-alcohol co-use on alcohol-induced blackouts and other alcohol-related consequences in college students

Abstract

Rationale

Alcohol-induced blackouts (AIBs) are common in college students and are associated with other alcohol-related consequences. Alcohol-nicotine co-use is also common in this population. Nicotine has cognitive-enhancing properties impacting multiple cognitive domains, including those impaired by alcohol (e.g., attention), but it is unclear whether nicotine affects AIB risk or the relationship between AIBs and other alcohol-related consequences.

Objectives

We examined the moderating effects of nicotine use on the associations between (a) alcohol and AIBs and (b) AIBs and other consequences (total and serious: sexual, legal, or those with potential to cause great harm).

Methods

College students who reported past semester heavy drinking and at least 1 AIB (N = 79, 55.7% female, 86.1% White) wore alcohol sensors and completed daily diaries over four consecutive weekends (89.9% completion). Multilevel models were conducted to test for moderating effects of nicotine (yes/no) on the alcohol-AIB relationship and the AIB-consequence relationship, adjusting for sex, race/ethnicity, and baseline nicotine use.

Results

Concurrent alcohol and nicotine use did not moderate the alcohol-AIB relationship, but weakened the associations between AIBs and both (1) total consequences and (2) serious consequences. On days with nicotine use, AIBs were associated with approximately 30% fewer total consequences and 50% fewer serious consequences than days without nicotine use.

Conclusions

College students experienced fewer total and serious consequences on AIB nights when nicotine was used compared to AIB nights when nicotine was not used. Future research should explore potential mechanisms underlying the observed effects.

Introduction

Alcohol-induced blackouts (AIBs) are characterized by periods of anterograde amnesia while an individual is still awake and interacting with their environment (White 2003). AIBs are common experiences among college student drinkers (Barnett et al. 2014a; Glenn et al. 2022). Approximately eight in ten student drinkers report at least one AIB during their four years of college (Glenn et al. 2022). AIBs occur frequently among students who report past semester AIBs and heavy episodic drinking (HED; 4+/5 + drinks on one occasion for females/males), at a rate of about one of every three drinking days (Richards et al. 2024). Risk factors for experiencing AIBs include psychological factors (e.g., willingness; Glenn et al. 2024; Litt et al. 2024) and behavioral factors (e.g., pregaming and playing drinking games; LaBrie et al. 2011; Richards et al. 2023c). The frequency at which AIBs are reported is especially concerning given their association with experiencing other alcohol-related consequences including non-serious (e.g., embarrassing oneself, being hungover; Merrill et al. 2019) and serious consequences (e.g., sexual violence, future alcohol use disorder, AUD; see Richards et al. 2023b; Studer et al. 2019; Valenstein-Mah et al. 2015; Voloshyna et al. 2018).

Concurrent use of alcohol and nicotine is a common and persistent trend among college students (Dierker et al. 2006; Samuolis et al. 2021; Yang et al. 2022). Studies suggest higher rates of nicotine use in people who misuse alcohol or have an AUD (Bobo and Husten 2000; Dobbs et al. 2020; Falk et al. 2006; Gelino et al. 2023). There are several potential explanations for these associations. One explanation may be that certain personality (e.g., impulsivity) or genetic traits increase risk for both alcohol and nicotine use and related disorders (Doran and Trim 2013; Hatoum et al. 2022; Lee et al. 2019; Moussa Rogers et al. 2021). Another explanation may be related to the psychopharmacologic interactions between alcohol and nicotine (Rose et al. 2004). There are mixed findings across laboratory and field studies suggesting that nicotine may decrease subjective sedative effects and increase stimulative effects of alcohol (Acheson et al. 2006; Kouri et al. 2004; Piasecki et al. 2011, 2012; Ralevski et al. 2012; Rose et al. 2004). Nicotine has been shown to enhance key components of attention (i.e., vigilant attention; Lawrence et al. 2002; Pham et al. 2020) which could serve to counteract the deleterious effects of alcohol on vigilant attention (Garrisson et al. 2021). While nicotine alone has been shown to improve memory performance (Froeliger et al. 2009; Kutlu and Gould 2015), results have been inconsistent on whether nicotine compensates for alcohol-related memory impairment. Studies in mice often show a positive effect of nicotine on memory following alcohol use (Abreu-Villaça et al. 2007; Jung et al. 2022). Studies in humans have shown mixed effects of nicotine on memory following alcohol use (Swan and Lessov-Schlaggar 2007).

The present study

As alcohol-nicotine co-use research expands, questions about their combined effect on acute alcohol-related consequences remain. The premise of the present secondary analysis is that the psychopharmacological effects of nicotine likely alter the drinking experiences and related outcomes of college students, including while actively experiencing an AIB. Effects of concurrent alcohol-nicotine use on AIB risk have been mixed. Mallett et al. (2017) reported increased AIB risk on co-use days, while Richards et al. (2023c) reported no significant effects. Whether nicotine changes the relationship between alcohol use and AIBs has not yet been explored. We were interested in understanding whether concurrent alcohol-nicotine use was associated with differential harm in the form of changes in the risk of AIBs. We hypothesized (H1) that the association between alcohol and AIBs would be stronger on alcohol-nicotine couse days compared to non-nicotine days. We formed this hypothesis using the following reasoning. First, the stimulating effects of nicotine would result in increased wakefulness and allow the individual to drink for longer durations and reach higher blood alcohol concentrations (Hershberger et al. 2021). Drinking duration has also been implicated as an independent risk factor for alcohol-related consequences including AIBs (Richards et al. 2024; Russell et al. 2022). Second, several studies examing nicotine on alcohol-induced memory impairment among nonabstinent nicotine users (i.e., current users) suggest a negative or null effect (Greenstein et al. 2010; Marshall et al. 2016; Ralevski et al. 2012).

We were also interested in understanding whether concurrent alcohol-nicotine use was associated with differential harm once an AIB has occurred. We explored this by examining whether nicotine use changed the association between AIBs and other alcohol-related consequences (total and serious). We hypothesized that the association between AIBs and (H2) total and (H3) serious alcohol-related consequences would be weaker on alcohol-nicotine co-use days compared to alcohol only days. We formed these hypotheses on the basis that the increased alertness caused by nicotine’s effects may make individuals more aware of their surroundings and decisions (Garrisson et al. 2021; Lawrence et al. 2002; Pham et al. 2020). This would therefore work to decrease risk for additional alcohol-related consequences even on occasions in which AIBs are experienced.

We examined whether these effects persist even after controlling for alcohol use level. We assessed both objective (i.e., transdermal alcohol concentration; TAC) and self-reported alcohol use level (i.e., drink counts), providing stronger assurance findings are due to the pharmacological effects of nicotine versus differences in drinking behavior (e.g., lower alcohol on days when co-use of nicotine occurred).

Methods

Participants and procedures

The study consisted of 79 participants ages 18–22 who regularly engaged in heavy drinking (Richards et al. 2024). We randomly selected 6000 students (50% sophomores and 50% juniors) from the university registrar’s database of students at a large, public university in the northeastern United States. Participants received an email with a description of the study inviting their participation. All recruitment materials (invitation email and up to 6 daily reminder emails) included a personalized URL to access a screening survey. Students were eligible to participate if they were aged 18–22 years, in their second- or third-years of college, reported drinking 4 or more drinks on a typical Friday or Saturday in the past semester, experienced at least one AIB in the past semester, owned an iPhone, and were willing to wear an alcohol sensor for 3 days each weekend (Thursday night through Sunday morning over 4 weekends). College students in their second and third years were included because this is a developmentally important time for alcohol risk (e.g., students move off campus, experience less monitoring/regulation, and approach their 21 st birthdays). Eligible students were immediately redirected to a 20-minute baseline survey. After baseline survey completion, students scheduled a 10-minute appointment to pick up the alcohol sensor and receive training the Monday-Thursday prior to the daily portion of the study beginning. Data collection was timed to avoid campus and community events associated with alcohol use (i.e., spring break, and final exam periods) in order to reflect typical drinking patterns. All procedures were approved by the University’s Institutional Review Board.

At screening, 15.5% (n = 927) of invited students consented to participate in the study of which 73.1% (n = 678) completed the screening. 28.3% (n = 192) of screened participants were eligible to continue, of which 100% completed the baseline survey. Due to device availability, 80 students were able to participate in the field period. Prior to the field period, one student dropped from the study, resulting in a final sample of 79 students.

Students were instructed to wear the sensor (BACtrack Skyn model T15) continuously starting Thursday evening at 5pm through Sunday morning when they woke up for four consecutive weekends, with the exception of removing the device to shower. The sensors can hold a charge for up to 10 days, so students were instructed to charge them once a week prior to Thursday. They received an email and text message at 4pm each Thursday reminding them to turn on the device and wear it by 5pm. Daily surveys were sent to all students each weekend morning (Friday-Sunday) at 10am for four weekends about the day/night before. Surveys were available until 6pm each day. Students received an email and text reminder each Monday prior to the start of each social weekend. An email reminder to complete the survey at 1pm and a text message reminder at 4pm each weekend day were also sent to students. There was an average completion rate of 89.9% across the 12 surveys (range: 1–12).

The mean age of participants at baseline was 20.1 years (SD = 0.9) and the majority identified as female (55.7%) and White (86.1%). Participants also identified as Hispanic/Latino (11.4%), Asian (5.1%), Black (1.3%), or multiracial (3.8%). The sample was split evenly between sophomores (49.4%) and juniors (50.6%). At baseline, 39.2% of participants were non-nicotine users, 22.8% were occasional nicotine users, and 38.0% were frequent nicotine users.

Measures

Alcohol-induced blackouts

The Alcohol-Induced Blackout Measure-2 (ABOM-2; Boness et al. 2022) assessed whether students experienced a fragmentary or en bloc AIB each night they reported drinking. The ABOM-2 scale was dichotomized for use on the daily level. Students were asked, “As a result of drinking yesterday, did you/were you ______”. Response options were dichotomous with “No” (0) and “Yes” (1). Fragmentary AIBs were assessed with 4 items (e.g., have fuzzy memories of events). En bloc AIBs were assessed with 4 items (e.g., unable to remember what happened the night before). The 8 items from the two types of AIBs were examined together due to low frequency of en bloc AIBs (n = 18 en bloc vs. n = 129 fragmentary AIBs reported). Responses from the 8 items were summed and recoded to a dichotomous scale with “No AIB reported” (0) if no items were endorsed and “Yes, AIB reported” (1) if ≥ 1 item was endorsed.

Alcohol-related consequences

Each drinking day, students were asked if they experienced 13 unique alcohol-related consequences (excluding AIBs) that came from the Brief Young Adult Alcohol Consequences Questionnaire (adapted for daily use; Kahler et al. 2005). Reported consequences were summed to get a daily total consequence score (see Supplemental Table 1).

Serious alcohol-related consequences

Serious alcohol-related consequences were defined based on previous literature, as consequences that were sexual, legal, or have great potential for personal harm (Richards et al. 2023b). Serious consequences (total of 8) included, “Pass out,” “Get in trouble with the police or [university] authorities for drinking,” “Find yourself in a situation where no one was sober enough to drive,” “Do something sexually you wouldn’t have done if you hadn’t been drinking,” “Have a sexual experience you wish you hadn’t,” “Wake up in an unexpected place,” “Get into a physical fight with someone,” and “Need a drink in the morning to get going.”

Nicotine use

Daily

Each weekend morning, students were asked if they used any other drugs/substances the previous day/night and if they said yes, they were asked which substance(s). The option for nicotine was stated as “Nicotine in any form (such as cigarettes, cigars, hookah, electronic devices, chewing tobacco, etc.)”. No nicotine use reported was coded as 0, nicotine use reported was coded as 1.

Baseline

At baseline, students reported how often they used nicotine in any form (such as cigarettes, cigars, hookah, electronic devices, chewing tobacco, etc.) in the past semester. Response options ranged from 0 (not in the past semester) to 12 or more times. Participants were categorized as non-nicotine users if they selected 0 (coded as 0), occasional nicotine users if they selected 1–11 (coded as 1), and frequent nicotine users if they selected 12 or more times (coded as 2).

Alcohol use

Transdermal alcohol concentration area under the curve (TAC AUC)

Details about initial TAC data cleaning have been previously published (Richards et al. 2024) and are available in the supplemental materials. Although multiple features are possible from TAC data, we considered only the AUC in the current analysis because AUC is most analogous to drink counts, representing the cumulative total biological alcohol exposure experienced by the person that day (Russell et al. 2022). AUC was extracted from each social day with TAC-positive episode data and calculated using $\sum \left[\left(TACit+TACi,t+1\right)/2\right]*\left[hoursi,t+1-hourst\right]$. (See Supplemental Materials for description of social days.) If a day contained multiple TAC episodes, AUC was calculated using all data for the day. TAC AUC was set to 0 if the sensor was worn for 80% or more of the hours of the social day but there were no episodes present (n = 232 days), as this suggested that no drinking had occurred. TAC AUC was left missing if no episodes were present but the sensor was worn for less than 80% of the hours of the social day (i.e., 10am-10am; n = 152 days).

Drink count

Each weekend morning, students were asked if they drank alcohol the previous night. If they selected yes, they were asked to select how many of six types of drinks (e.g., beer, mixed drink, wine) they had on a scale of 0 to 10 or more. Amounts of each type of drink reported were summed to get a total drink count.

Sex

Sex at birth (female [0] or male [1]) was assessed at the baseline survey.

Race/Ethinicity

Race and ethnicity (dichotomized for analyses as non-Hispanic/Latino White [0] vs. racial and ethnic minority groups [1]) was assessed at the baseline survey.

Statistical analysis

All data analyses were conducted in R. Descriptive statistics (X², t-tests, ANOVA) were conducted to examine whether person-mean drink count, AIBs, consequences, or nicotine use significantly differed by baseline nicotine use. Additional descriptive statistics were conducted on a subset of data including only participants who used nicotine as least once during the study (n = 38) to examine whether day-level drink count, AIBs, or consequences significantly differed by whether nicotine was used that day. While our primary hypotheses involved moderation, each model was first conducted without an interaction term to test for main effects. To test whether nicotine moderated the association between alcohol use (TAC AUC or drink count, in separate models) and AIBs (H1), multilevel logistic models controlling for sex, race/ethnicity, and baseline nicotine use were conducted. To test whether nicotine moderated the relationship between AIBs and consquences (H2 and H3), multilevel linear models controlling for alcohol use (TAC AUC or drink count, in separate models), sex, race/ethnicity, and baseline nicotine use were conducted. Separate models were estimated to examine total consequences (H2) and serious consequences (H3). Each model had three levels of variation (day, week, person) to reduce the risk of Type I errors. We used a three-level centering strategy to partition the variance of each predictor (AIBs, nicotine, TAC AUC, drink count) into day-, week-, and person-levels. Raw values were centered on person-day-means (creating a day-level variable), person-week-means were centered on person-means (creating a week-level variable), and person-means were centered on the grand mean (creating a person-level variable). TAC AUC was z-scored to aid in interpretability. Covariates were grand-mean centered. Each model included random intercepts and random slopes for day-level nicotine and alcohol use (H1) or AIBs (H2 and H3) at the week- and person-levels. Models were estimated in a Bayesian framework (with non-informative priors and 20,000 total iterations [50% warmup]) using the brms package in R (Bürkner 2017). Bayesian models were used to support convergence in models containing correlated random effects. Non-informative priors were used to allow the data to dominate the estimation and produce results that are asymptotically equivalent to maximum likelihood (Hamaker et al. 2018). Model estimates are the means of the posterior parameter distributions and significance is determined using 95% equal-tailed credible intervals (CIs). Estimates with CIs that did not contain 0.00 (to the second decimal place) were considered significant.

Results

Descriptive statistics

Descriptive statistics on the variables of interest are presented in Table 1. Frequencies of specific consequences experienced are presented in Supplemental Table (1) Test statistics and means or frequencies of self-reported drinks, AIBs, total consequences, and serious consequences by baseline nicotine use and by nicotine-alcohol co-use during the study period are presented in Supplemental Table (2) AIBs and total consequences differed significantly by baseline nicotine use, but drink count and serious consequences did not. Among those who co-used nicotine and alcohol during the twelve day study period at least once (n = 38), nicotine use was associated with drink count and AIBs, but not total consequences nor serious consequences.

Table 1.

Descriptive statistics for day-level study variables of interest over twelve social weekend days

	Frequency (%) or Mean (SD) on Drinking Days (n = 501)	Frequency of Students to Report At Least Once (%) (n = 79)	Mean Per Student (SD)
Drink Count	6.54 (4.33)	79 (100%)	6.39 (2.87)
Alcohol-Induced Blackouts	147 (29.34%)	59 (74.68%)	1.86 (1.85)
Total Consequences	0.90 (1.30)	70 (88.61%)	5.73 (4.96)
Serious Consequences	0.17 (0.50)	34 (43.04%)	1.10 (2.00)
Nicotine Use (yes/no)	184 (37.02%)	38 (48.10%)	2.33 (3.20)

H1: Alcohol-induced blackouts

Day-level fixed effects examining the main and moderating effects of nicotine on AIB risk are presented in Table 2. Results from the main effects and moderated full models are presented in Supplemental Tables 3 and 4. Model 1 presents the main effects (i.e., unmoderated) of alcohol use (TAC AUC or drink count) and nicotine on AIB risk. Supplemental Tables 3 and 4, model 2 present the interactive effects of alcohol use and nicotine on AIB risk. Nicotine did not change the relationship between alcohol and AIBs when controlling for other variables (e.g., alcohol use, baseline nicotine).

Table 2.

Results from multilevel logistic models predicting alcohol-induced blackouts, controlling for TAC

	Model 1: Main Effects Model		Model 2: Moderation Model
	Estimate	95% CI	Estimate	95% CI
Fixed effects Intercept	0.14	0.07, 0.24	0.14	0.07, 0.24
Daily nicotine	1.91	0.09, 36.79	2.34	0.10, 47.53
Daily TAC AUC	2.69	1.87, 4.12	2.76	1.89, 4.30
Sex	0.60	0.24, 1.42	0.59	0.24, 1.43
Race/ethnicity	0.18	0.04, 0.61	0.18	0.04, 0.54
Baseline nicotine	1.49	0.71, 3.18	1.50	0.71, 3.33
Daily nicotine*TAC AUC	--	--	0.61	0.07, 4.29

CI = credible interval, SD = standard deviation, TAC AUC = transdermal alcohol concentration area under the curve

^aNicotine and TAC AUC are week-mean centered; TAC AUC was z-scored to increase interpretability; covariates were grand-mean centered

^bFixed effects were exponentiated to obtain odds ratios; Bold values indicate significant fixed effects (95% CI does not contain 1)

H2: Total consequences

Day-level fixed effects examining nicotine as a moderator of the relationship between AIBs and total consequences are presented in Table 3, Model 1. Results from the main effects and moderated full models are presented in Supplemental Tables 5–8, Model 1. Figure 1 depicts the simple slopes between AIBs and total consequences with and without nicotine use. The main effects model showed that drinking days with an AIB were associated with an average of 1.26 additional consequences compared to days without an AIB. There was a significant negative moderating effect of nicotine. On days with nicotine use, AIBs were associated with approximately 30% fewer total consequences than when nicotine was not used.

Table 3.

Day-level fixed effects from multilevel linear models predicting alcohol-related consequences from alcohol-induced blackouts with nicotine use as a moderator

	Model 1: Total Consequences		Model 2: Serious Consequences
	Estimate	95% CI	Estimate	95% CI
Fixed effects
Intercept	0.87	0.70, 1.03	0.17	0.09, 0.25
Daily AIB	1.33	0.85, 1.81	0.30	0.07, 0.55
Daily Nicotine	−0.09	−0.66, 0.47	0.13	−0.11, 0.36
Daily TAC AUC	0.19	0.08, 0.30	0.05	0.00, 0.09
Sex	−0.12	−0.44, 0.18	0.10	−0.04, 0.24
Race/Ethnicity	−0.29	−0.67, 0.12	−0.06	−0.25, 0.12
Baseline Nicotine	0.11	−0.14, 0.38	0.15	0.04, 0.27
Daily AIB*Nicotine	−3.49	−5.56, −1.41	−1.40	−2.32, −0.53

AIB = alcohol-induced blackout, CI = credible interval, SD = standard deviation, TAC AUC = transdermal alcohol concentration area under the curve

^aDaily AIB, nicotine, and TAC AUC were week-mean centered; TAC AUC was z-scored to increase interpretability; covariates were grand-mean centered

^bBold values indicate significant fixed effects (95% CI does not contain 0)

Fig. 1.

Simple slopes of the relationship between daily alcohol-induced blackouts and total alcohol-related consequences by nicotine use

H3: Serious consequences

Day-level fixed effects examining the nicotine as a moderator of the relationship between AIBs and serious consequences are presented in Table 3, Model 2. Results from the full main effects and moderated models are presented in Supplemental Tables 5–8, Model 2. Figure 2 depicts the simple slopes between AIBs and serious consequences with and without nicotine use. The main effects model showed that drinking days with an AIB were associated with an average of 0.27 additional serious consequences than days without an AIB. There was a significant negative moderating effect of nicotine. On days with nicotine use, AIBs were associated with approximately 50% fewer serious consequences than when nicotine was not used. Of note, the association between AIBs and serious consequences was not significant when nicotine was used.

Fig. 2.

Simple slopes of the relationship between daily alcohol-induced blackouts and serious alcohol-related consequences by nicotine use

Discussion

This study was an initial investigation examining whether concurrent alcohol and nicotine use increased or decreased alcohol-related harm. We observed no significant changes in the alcohol-AIB relationship when nicotine use was endorsed (H1). However, the relationship between AIBs and alcohol-related consequences was weakened when nicotine use was endorsed (H2 and H3). AIB days with nicotine use resulted in nearly 30% fewer total consequences and 50% fewer serious consequences, on average, compared to AIB days without nicotine use. These early findings suggest that nicotine may not protect against AIBs, but may reduce the likelihood of experiencing additional alcohol-related consequences during AIB episodes. The need for additional research investigating these mechanisms is warranted.

The present investigation to assess the impact of nicotine on AIBs and other alcohol-related consequences builds upon previous work on the co-use of alcohol and nicotine. These findings are consistent with findings that nicotine increases stimulation, vigilant attention and other cognitive domains (Heishman et al. 2010). This may translate into individuals being more aware of their surroundings and actions while drinking. In turn, this may enhance their ability to avoid participating in activities or interacting with individuals who do not have their best interest in mind, resulting in the avoidance or reduction of alcohol-related consequences. While nicotine use has inconsistently been associated with greater odds of experiencing an AIB (Mallett et al. 2017; Richards et al. 2023c), our results suggest nicotine may reduce the likelihood of other consequences during or after an AIB.

It is possible that this observed protection may translate into drinking more than they normally would if they were not using nicotine, as potentially aversive alcohol-related consequences are avoided. Previous research also suggests that experiencing fewer negative consequences is associated with increased willingness to drink the next day (LoParco et al. 2021). The short-term protective effects of nicotine may create a false sense of security for students and put them at risk of other acute and chronic problems. Alcohol and nicotine seem to interact in a manner that may promote heavier use within a given episode (Oliver et al. 2013). Laboratory studies suggest that alcohol cues can increase the urge to use nicotine, and nicotine cues can increase the urge to use alcohol (Dermody and Hendershot 2017; Verplaetse and McKee 2017). It has also been shown that alcohol increases nicotine self-administration in the laboratory but results for the reciprocal relationship are mixed (Dermody and Hendershot 2017; Verplaetse and McKee 2017).

As with many early investigations and secondary data analyses, the parent study was not designed to answer the questions posed. Our findings are informative and support further investigation into the effects of concurrent alcohol-tobacco use on acute alcohol-related harm. The following limitations should be addressed in future investigations of alcohol-nicotine co-use. First, because the parent study was not related to nicotine, our nicotine-related measures were limited. We were not able to differentiate between different types of nicotine use (e.g., combustible cigarettes vs. electronic cigarettes). We did not have information about the dosing of nicotine and were therefore unable to investigate and dose-response relationships between alcohol and nicotine. We did not have data on the timing of nicotine use and were unable to distinguish between simultaneous use versus same day use. It is possible that students used alcohol and nicotine on the same day but at different times. We were also unable to examine which substance was used first. Our baseline measure of nicotine use was also limited to a range of 0 nicotine use days to 12 or more over the past semester. We used this information to categorize baseline nicotine to the best of our ability, but more detailed data on whether someone is truly an occasional versus regular nicotine user may be valuable. Second, the sample consisted of primarily White college students who reported recent heavy drinking and AIBs. It is important to continue these investigations in more diverse populations. Third, the study followed 79 students for four weekends over a one-month period during one season. It is possible that the drinking observed in this study may not be representative of the students’ “true” drinking patterns. The relatively small, non-diverse sample also limited analyses examining whether these relationships differed by important demographic factors including sex and race. Additional research with a larger, sample and longer follow-up periods is warranted. Fourth, it is possible that the sensors missed lower intensity drinking days (Barnett et al. 2014b). There is currently no “gold standard” algorithm for detecting drinking days for the Skyn sensor. Our guidelines for detecting drinking days were informed by previous studies (Courtney et al. 2023; Didier et al. 2024; Richards et al. 2023a). Fifth, we were unable to examine differences between AIB type due to a very low number (n = 18) of en bloc AIBs.

Conclusion

In this early investigation, nicotine did not modify the relationship between alcohol and AIBs, but did weaken the relationship between AIBs and other alcohol-related consequences. College students experienced fewer total and serious consequences on AIB nights when nicotine was used compared to AIB nights when nicotine was not used. These results suggest that alcohol-nicotine co-use may allow individuals to drink more without feeling/experiencing the negative effects. This may ultimately lead to increased risk for other, more chronic, alcohol-related problems. Our findings support the need for future research that assesses nicotine use method, dosing, and timing relative to alcohol consumption. Additional investigations of the cognitive and behavioral mechanisms through which nicotine may reduce AIB-related harm are warranted. Continued investigations are also needed to understand the long-term effects of alcohol-nicotine co-use.

Supplementary Material

The online version contains supplementary material available at https://doi.org/10.1007/s00213-025-06830-x.

Data availability

Data will will be made available upon request to the corresponding author.