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. Author manuscript; available in PMC: 2026 Jun 1.
Published in final edited form as: Addict Behav. 2025 Feb 10;165:108286. doi: 10.1016/j.addbeh.2025.108286

Predictors and consequences of unplanned drinking among young adults

Brian Suffoletto 1, Tammy Chung 2
PMCID: PMC12207992  NIHMSID: NIHMS2058252  PMID: 39954482

Abstract

Objective:

Unplanned drinking, or drinking that violates intentions to limit alcohol consumption, has been linked to significant alcohol-related consequences in college students; however, predictors and outcomes remain incompletely understood among other populations of young adults. This study identified person- and event-level predictors of unplanned drinking and explore the association of unplanned drinking with negative alcohol-related consequences in a racially and educationally diverse cohort of young adults.

Method:

A total of 938 young adults (aged 18–25; 70% female; 60% non-college; 37% Black) participating in a randomized trial testing text-message alcohol interventions completed ecological momentary assessments (EMA) reporting drinking intentions and alcohol consumption twice weekly for at least 4 weeks over a 14 week period. Controlling for intervention effects, mixed-effects models examined predictors of unplanned drinking days, and zero-inflated negative binomial regression models assessed the relationship between frequency of unplanned drinking and negative alcohol consequences at a 14-week follow-up.

Results:

Participants reported alcohol consumption on 16.9% of days when they had no plan to drink. Odds of unplanned drinking was higher for older age (adjusted Odds Ratio [aOR] = 1.06, p < 0.01), Black race (aOR = 1.25, p < 0.01), higher AUDIT-C score (aOR = 1.14, p < 0.001), and higher negative urgency score (aOR = 1.05, p < 0.01), weekends (aOR = 1.63, p < 0.001) and the presence of friends drinking (aOR = 9.37, p < 0.001). Compared to participants in the lowest unplanned drinking day category, those in the highest category showed a 26% higher negative alcohol consequence rate ratio (RR = 1.26, 95% CI [1.07, 1.48]).

Conclusions:

Unplanned drinking in young adults is strongly influenced by social context and individual risk factors. This behavior, when extreme, was associated with increased negative alcohol-related consequences. Interventions targeting impulsivity and peer influence may reduce unplanned drinking and mitigate its harmful effects.

Keywords: young adult, alcohol use, peer influence

1. Introduction

Young adulthood is a critical developmental period marked by increased susceptibility to peer influence (Watts et al., 2024), and high levels of alcohol consumption (Substance Abuse and Mental Health Services Administration, 2024). These factors often converge in behaviors such as unplanned drinking—consuming alcohol despite prior intentions not to drink. According to the Model for Unplanned Drinking (Pearson & Henson, 2013), unplanned drinking behavior is particularly risky because it often occurs in the absence of having harm-reduction strategies in place, potentially leading to more severe alcohol-related consequences. Despite its potential significance as a target for intervention, much remains unknown about unplanned drinking.

One significant gap in the literature is the frequency of unplanned drinking among racially and educationally diverse young adults. Studies of predominantly White college students estimate that 20%–40% of drinking occasions are unplanned (Dvorak et al., 2014; Fairlie et al., 2019). However, it remains unclear how often unplanned drinking occurs in other populations. For instance, young adults not in college may have unique risks, such as increased exposure to environments where alcohol is more readily available and fewer protective institutional structures like campus regulations (Carter et al., 2010). Additionally, non-college young adults may experience heightened chronic stress related to finances or work (Ryu & Fan, 2023), which can accelerate hazardous drinking (McMullin et al., 2021).

Another critical unknown is the influence of impulsivity traits on unplanned drinking. The dual process model (Stacy & Wiers, 2010) suggests that unplanned drinking is driven by automatic processes, likely exacerbated by impulsivity. Research shows that state impulsivity is related to alcohol use (Coskunpinar et al., 2013) and that trait-level impulsivity, specifically lack of premeditation, is associated with drinking (Griffin & Trull, 2020). However, findings related to unplanned drinking are scarce. For instance, one study examined state-level impulsivity and found that moderate drinkers aged 18–45 with psychiatric disorders who were high in momentary negative urgency were less likely to engage in same-day unplanned drinking, but consumed more alcohol when they did (Griffin et al., 2021).

The role of peer influence on unplanned drinking is also unclear. Social Cognitive Theory (Bandura, 1991) suggests that peers are a major contextual factor shaping alcohol use among young adults (Thrul & Kuntsche, 2015). However, findings linking peers with unplanned drinking remain mixed. One EMA study reported no relationship between drinking in social settings and the likelihood of unplanned heavy drinking (Fairlie et al., 2019), while another found that college students were more likely to drink, and consume more, on unplanned drinking days when they were with others (Howard et al., 2024). Both studies focused on college-attending young adults, excluding non-college populations who may exhibit different drinking patterns.

The relationship between unplanned drinking and alcohol-related consequences is similarly inconclusive. One EMA study found that unplanned heavy drinking days were associated with more negative alcohol consequences compared to planned drinking days (Fairlie et al., 2019). Yet, more recent research suggests that after controlling for the number of drinks consumed, unplanned drinking may be linked to fewer alcohol-related consequences (Lauher et al., 2020). These studies compare unplanned drinking days to planned drinking days, which may not fully capture the cumulative risks posed by frequent unplanned drinking. A more thorough approach examining how the frequency of unplanned drinking within an individual relates to consequences is warranted.

2. Present study

We conducted a secondary analysis of data from a 5-arm interventional trial to identify person- and event-level predictors of unplanned drinking days in a diverse sample of young adults and examine the relationship between the frequency of unplanned drinking and negative alcohol-related consequences. We hypothesized that impulsivity traits (at the person level) and the presence of friends drinking (at the event level) would increase the odds of unplanned drinking. We hypothesized that individuals with a higher frequency of unplanned drinking would experience more negative alcohol-related consequences at the 14-week follow-up, controlling for overall alcohol consumption and baseline consequences. Our study addresses gaps in existing research by including a racially and educationally diverse cohort of young adults and utilizing longitudinal EMA data over a longer period than previous studies. The larger sample size and use of multilevel models provide a more robust analysis of individual variability in unplanned drinking behavior, contributing to a more comprehensive understanding of this phenomenon across subpopulations of young adults.

3. Methods

3.1. Participants and procedures

Data were collected from young adults participating in a randomized trial testing five text-message interventions (pre-registered at clinicaltrials.gov: NCT02918565). The study protocol was approved by the University of Pittsburgh Human Research Protection Office. Participants were recruited from Emergency Departments (EDs) in Western Pennsylvania between August 2017 and September 2021. Eligible patients aged 18 to 25 met inclusion criteria of an Alcohol Use Disorder Identification Test for Consumption (AUDIT-C) score of ≥3 for women or ≥4 for men, and at least one binge drinking day (defined as consuming >4 drinks for women or >5 drinks for men in one day) (Binge Drinking | CDC, 2022) in the past 30 days. Patients did not need to be interested in reducing their drinking to participate. Participants could be receiving care in the ED for any reason. Exclusion criteria included prior addiction treatment, current psychiatric treatment, or not owning a phone with text messaging capability.

Participants completed a baseline assessment in the ED, including a 30-day Alcohol Timeline Follow Back (TLFB) calendar to identify the two days each week when they typically drank the most alcohol. EMA collection began in the week of enrollment and continued for a 14-week period. EMA focused on pre-drinking intentions and next-day alcohol consumption, which guided intervention feedback messages. At 3 p.m., participants were prompted to report their drinking plans for the day. At 12 p.m. the next day, they were asked to recall their alcohol consumption. During the first two weeks (pre-trial run-in period), no feedback was provided based on these responses. In the subsequent 12 weeks, EMA responses tailored feedback messages according to the participant’s assigned intervention group. For participants enrolled during COVID-19 isolation mandates, additional EMA questions inquired about the presence of friends and whether those friends were drinking. Upon completing the 14-week EMA protocol, participants were asked to complete a web-based assessment survey.

3.2. Measures

3.2.1. Person-level

Demographics.

At baseline, participants provided information on their sex at birth (coded 1=male, 0=female), age, college enrollment status (coded 1=yes, 0=no), and race (coded as 0=White, 1=Black, 2=Asian, 3=Mixed, 4=Other).

Alcohol use severity.

Baseline alcohol use severity was measured using the Alcohol Use Disorder Identification Test for Consumption (AUDIT-C), a validated 3-item instrument (Bradley et al., 2007; Bush et al., 1998). The AUDIT-C assesses alcohol consumption frequency, typical quantity consumed, and frequency of binge drinking episodes. Each item is scored from 0 to 4, resulting in a total score ranging from 0 to 12, where higher scores indicate greater severity of alcohol use.

Impulsivity.

Impulsivity was assessed using an abbreviated version of the UPPS-P Impulsive Behavior Scale (Cyders et al., 2014). This 20-item scale measures five dimensions of impulsivity: (1) negative urgency (NU), (2) positive urgency (PU), (3) sensation seeking (SS), (4) lack of perseverance (PE), and (5) lack of premeditation (PR). Items are rated on a 4-point Likert scale (1 = strongly disagree to 4 = strongly agree), and scores are computed by summing the responses for each subscale. Higher scores reflect higher levels of impulsivity. Cronbach’s alpha coefficients for the subscales in this study were as follows: NU=0.82, PU=0.87, SS=0.71, PE=0.80, and PR=0.86.

Alcohol consequences.

Negative alcohol-related consequences were measured at baseline and follow-up using the Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ) (Kahler et al., 2008). This 24-item measure assesses a range of alcohol-related problems (e.g., blackouts, hangovers) using a dichotomous (present/absent) response format. Higher scores indicate a wider variety and more severe alcohol-related problems. Cronbach’s alpha for the B-YAACQ in this sample was 0.87.

Intervention exposure.

Intervention exposure was categorized based on five different intervention arms described in a prior publication (Suffoletto et al., 2022). The interventions were coded as follows: 0 = TRACK (self-monitoring of drinking plans, desire to get drunk, and drinking quantity); 1 = PLAN (feedback on drinking plans and desire to get drunk); 2 = USE (feedback on drinking quantity); 3 = GOAL (prompts to set personal drinking goals, tips to achieve goals, and feedback on goal attainment); 4 = COMBO (a combination of all features).

3.2.2. Event-level

Drinking Plan.

At 3 p.m. each day, participants were asked whether they planned to drink that day via the following prompt: “Do you plan on drinking alcohol today?” Responses were coded as 0 (no plan) or 1 (plan to drink).

Alcohol consumption.

The next day at 12 p.m., participants were asked to recall their alcohol consumption from the previous day using the question: “How many drinks did you have yesterday?” A definition of standard drinks (NIAAA, n.d.) was provided during the enrollment process. Alcohol consumption was treated as a continuous (count) variable. This single-item measure of drinking quantity has been successfully validated in previous studies, where it correlated positively with the Timeline Followback (TLFB) measure (Suffoletto et al., 2012).

Unplanned drinking days.

An unplanned drinking day was defined as a day when the participant reported no plan to drink (plan = 0) but consumed alcohol (drinking quantity > 0). The comparator was defined as a day when the participant reported no plan to drink and consumed no alcohol (drinking quantity = 0). We also calculated the proportion of unplanned drinking days by dividing the total number of unplanned drinking days by the total number of days when no drinking was planned.

Social contact.

A subset of participants (n=111) were asked the following question: “How many friends were you with yesterday?” For participants who reported being with friends, a follow-up question asked: “How many of these friends were drinking alcohol?” Responses to these questions were used to generate a three-level variable to indicate type of social contact on that day (0=no friend contact; 1=non-drinking friend contact; 2=drinking friend contact).

Day of week.

We categorized assessments occurring on Fridays and Saturdays as “weekend” days and those on Sundays through Thursdays as “weekday” days (0 = weekday, 1 = weekend).

3.3. Data analysis

Analysis of Predictors of Unplanned Drinking.

To examine the effects of person-level and event-level characteristics on the likelihood of unplanned drinking days, we employed mixed-effects logistic regression models. This approach is appropriate given the nested structure of the data, where daily observations (Level 1) are nested within individuals (Level 2). These models account for both within-person variability (day-to-day differences) and between-person variability (individual-level factors).

We specified random intercept models to capture unobserved heterogeneity in baseline likelihood of unplanned drinking days across individuals. The random intercepts control for the correlation of repeated measures within each individual. Fixed effects were used to model both person-level predictors and event-level predictors.

The primary fixed effects of interest included the following predictors that had a significant univariate association with unplanned drinking at a p-value<0.05, as follows:

  • Person-level variables (Level 2): Age; sex; race; baseline AUDIT-C score; baseline negative urgency; treatment arm. Other impulsivity scales did not have a significant univariate association with unplanned drinking and were not included in the final models. College attendance was forced into the models regardless of significance to adjust for potential confounding effects and to explore interaction terms with other person-level predictors. Intervention by time interaction was significant and was included in final models.

  • Event-level variables (Level 1): Weekend (vs. weekday); Social contact category; day of intervention exposure (time). We did not find that participants who provided social contact data (n=111) differed from participants who did not (n=827); see Supplemental Table 1.

To estimate the proportion of variance in unplanned drinking attributable to between-person differences (Level 2), we computed the intraclass correlation coefficient (ICC). The ICC was calculated using the variance of the random intercept and the fixed day-level logistic regression variance (π2/3 ≈ 3.29). For each model, odds ratios (ORs) and 95% confidence intervals (CIs) were reported to quantify the effects of predictors on the likelihood of unplanned drinking days. Model fit was evaluated using the likelihood ratio test, comparing mixed-effects models with standard logistic regression models. Statistical significance was set at p < 0.05.

In a sensitivity analysis, missing drinking quantity data (11.6% of observations) were imputed using a sequential approach that prioritized temporal proximity and preserved individual drinking patterns. First, missing values were imputed using observed values from the same day of the prior week. Any remaining missing values were then sequentially imputed using: the same day of the following week, the previous day, the following day, two weeks prior, and two weeks after. For any remaining missing values, if the participant had any recorded drinking days (quantity > 0), the participant’s mean non-zero drinking quantity was used. If the participant had any recorded non-drinking days (quantity = 0), remaining missing values were imputed as zero. This approach maintained the temporal structure of drinking patterns while accounting for individual differences in drinking behavior.

Analysis of Negative Alcohol Consequences

To examine the relationship between person-level and event-level predictors and the count of negative alcohol-related consequences at the 14-week follow-up, we employed a zero-inflated negative binomial (ZINB) regression model. The ZINB model is appropriate for overdispersed count data with an excess of zero counts. This model allowed us to model two processes simultaneously. The count model estimated the expected number of negative alcohol consequences for individuals at risk of non-zero counts using a negative binomial distribution. The zero-inflation model predicted the probability that an individual belonged to the “always zero” group (i.e., individuals who would not experience any negative alcohol consequences) using logistic regression.

4. Results

4.1. Person-level characteristics.

The sample of 938 participants had a mean age of 22.1 years (SD = 2.1); the majority were female (70%) and identified as Black (36.6%). In terms of education, 60% of participants were not enrolled in a four-year college program. The mean AUDIT-C score was 6.2 (SD = 2.0), with 21.2% of participants having a score of 8 or higher, indicating high-risk drinking (Khadjesari et al., 2017). Participants were assigned to TRACK (n=195), PLAN (n=190), USE (n=193), GOAL (n=174) and COMBO (n=186). Regarding unplanned drinking patterns, 23% of participants reported no unplanned drinking days; 48.3% had unplanned drinking on 1–25% of their drinking days; 21.2% had unplanned drinking on 25–50% of days; and 7% reported unplanned drinking on the majority (50–100%) of their drinking days.

4.2. Day-level characteristics.

Participants each contributed a mean of 12 (SD = 6) days with no plan to drink alcohol. They reported drinking on 21.3% of these days. On unplanned drinking days, participants consumed an average of 2.5 drinks (SD = 1.9), and 34.6% of these days were categorized as binge drinking days. Unplanned drinking was significantly more common on weekends, with 82.5% of unplanned drinking days occurring on Fridays or Saturdays. Among the subset of participants (n=111) who provided data on social contacts, in-person contact with non-drinking friends occurred on 11% of days and contact with drinking friends occurred on 21% of days.

4.3. Predictors of unplanned drinking days

In this multilevel model examining drinking outcomes, several significant predictors emerged at both the between- and within-person levels (see Table 1: Model 1). At the between-person level (ICC = 0.53, indicating that 53% of the variance was attributable to stable individual differences), older age was associated with slightly higher odds of drinking (OR = 1.06, 95% CI [1.01, 1.07]), while African American participants showed 25% higher odds compared to White participants (OR = 1.25, 95% CI [1.04, 1.50]). Higher AUDIT-C scores and negative urgency were both associated with increased odds of drinking (OR = 1.14, 95% CI [1.09, 1.19] and OR = 1.05, 95% CI [1.02, 1.08], respectively). For the intervention effects over time, both the USE and COMBO conditions showed significantly reduced odds compared to TRACK (OR = 0.96, 95% CI [0.93, 0.99] and OR = 0.94, 95% CI [0.91, 0.97], respectively). At the within-person level, drinking was more likely on weekends, with 63% higher odds compared to weekdays (OR = 1.63, 95% CI [1.37, 1.94]). Sensitivity analyses using imputed drinking quantity values produced similar significant estimates for age, AUDIT-C scores, baseline negative urgency scores, and weekend; however race and intervention no longer had significant associations with unplanned drinking (see Supplemental Table 2).

Table 1:

Predictors of Unplanned Drinking Days

Model 1 Cases (n) M (SD) or % Estimate (SE) z Odds ratio [95% CI]
Level 2 (between person)
Age 938 22.1 (2.1) 0.06 (0.02) 2.62** 1.06 [1.01, 1.07]
Female birth sex 655 70 0.11 (0.40) 0.61 0.97 [0.80, 1.17]
Race
 White/Caucasian 529 54.4 REF REF
 African American 328 36.6 0.23 (0.09) 2.65** 1.25 [1.04, 1.50]
 Mixed 9 0.9 (−)0.11 (0.40) −0.28 0.98 [0.43, 2.31]
 Asian 45 5.3 0.05 (0.19) 0.29 0.89 [0.74, 1.52]
 Other 26 2.8 0.27 (0.24) 1.23 1.32 [0.74, 2.02]
Current college enrollment 368 39.2 0.06 (0.09) 0.63 1.02 [0.85, 1.23]
AUDIT-C score 938 6.2 (2.0) 0.15 (0.02) 6.84*** 1.14 [1.09, 1.19]
Negative urgency score 938 8.6 (3.0) 0.06 (0.01) 4.80*** 1.05 [1.02, 1.08]
Intervention × time
 TRACK 195 20.8 REF REF
 PLAN 190 20.3 −0.02 (0.02) −0.92 0.98 [0.70, 1.39]
 USE 193 20.6 −0.04 (0.02) −2.14* 0.96 [0.93, 0.99]
 GOAL 174 18.6 −0.01 (0.02) −0.83 0.99 [0.95, 1.09]
 COMBO 186 19,8 −0.06 (0.02) −3.41*** 0.94 [0.91, 0.97]
Level 1 (within person)
Weekday 2,699 23.5 REF
Weekend 8,789 76.5 0.51 (0.07) 1.63*** 1.63 [1.37, 1.94]
Model 2
No friend contact 716 68.7 REF REF
Friend contact (no drinking) 110 10.7 1.41 0.88 1.41 [0.66, 2.99]
Friend contact (drinking) 213 20.6 12.7 9.37*** 12.7 [7.45, 21.55]
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Table 1 Appendix: M=mean; SD= standard deviation; SE=standard error; CI= confidence interval; REF= reference.

*

p<.05,

**

p<.01;

***

p<.001

4.4. Peer Influence on Unplanned Drinking

Among the subset of participants who provided data on social contacts (n=111), in the multilevel model controlling, at the day-level, relative to those who reported no friends present (reference category), participants who reported drinking friends present had substantially higher odds of unplanned drinking (OR = 12.67, 95% CI [7.45, 21.55], p < .001). Non-drinking friends showed a non-significant trend toward higher odds of unplanned drinking (OR = 1.41, 95% CI [0.66, 2.99], p = .377). These effects were observed while controlling for demographic characteristics, baseline drinking (AUDIT scores), and intervention conditions. (see Table 1: Model 2).

4.5. Predictors of negative alcohol consequences

A zero-inflated negative binomial regression model examined predictors of negative alcohol-related consequences in 769 participants (82%) who completed the 14-week follow-up assessment (Table 2). The mean number of negative alcohol-related consequences at follow-up was 2.7 (SD = 3.8), with a range of 0 to 27 consequences reported. In the count portion of the model, several significant predictors emerged. AUDIT scores were positively associated with negative consequences, with each one-point increase in AUDIT corresponding to a 7% increase in the rate of consequences (RR = 1.07, 95% CI [1.04, 1.09]). Baseline drinking quantity was also positively associated, with each additional drink at baseline associated with an 8% increase in consequences (RR = 1.08, 95% CI [1.07, 1.10]). Compared to participants in the lowest unplanned drinking day category (n=220), those in the highest category (n=66) showed a 26% higher rate of negative consequences (RR = 1.26, 95% CI [1.07, 1.48]). Treatment conditions GOAL and COMBO were associated with reduced negative consequences compared to control, showing 19% (RR = 0.81, 95% CI [0.70, 0.94]) and 14% (RR = 0.86, 95% CI [0.74, 1.00]) lower rates, respectively. The inflation component of the model, which estimates the probability of excess zeros beyond what would be expected from the negative binomial distribution, indicated a low probability of excess zeros (b = −2.82, SE = 0.24, p < .001), corresponding to an estimated 5.6% of observations being excess zeros.

Table 2:

Predictors of Number of Negative Alcohol Consequences

Characteristic Estimate (SE) z
Age −0.01 −0.81
Female birth sex 0.04 (0.06) 0.65
Race
 White/Caucasian REF
 African American 0.02 (0.06) 0.38
 Mixed −0.01 (0.27) −0.04
 Asian −0.09 (0.13) −0.7
 Other −0.39 (0.16) −2.46
Current college enrollment 0.02 (0.06) 0.02
AUDIT-C score 0.06 (0.02)
Negative urgency score 0.02 (0.01)
Baseline negative alcohol consequences 0.08 (0.01) 12.3***
Intervention × time
 TRACK
 PLAN −0.09 (0.08) −1.17
 USE −0.09 (0.08) −1.23
 GOAL −0.21 (0.08) −2.60**
 COMBO −0.18 (0.08) −2.23**
Proportion days with UPDD
 None REF
 Low 0.01 (0.07) 0.91
 Moderate 0.11 (0.08) 1.41
 High 0.37 (0.10) 3.60***
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Notes: UPDD= Unplanned Drinking Day; SE=standard error; REF= reference.

*

p<.05,

**

p<.01;

***

p<.001

5. Discussion

5.1. Main findings

This study examined person- and event-level predictors of unplanned drinking and their relationship to negative alcohol-related consequences in a diverse sample of young adults. As hypothesized, unplanned drinking was prevalent, especially in social contexts involving friends and during weekends. Key person-level factors that predicted about one-half of the variability in unplanned drinking included older age, Black race, higher baseline AUDIT-C scores, and elevated baseline negative urgency scores. Event-level factors that predicted the other half of the variability included weekend days and contact with friends, particularly when friends were drinking alcohol. Moreover, the highest frequency of unplanned drinking was associated with a greater number of negative alcohol-related consequences at the 14-week follow-up, highlighting the potential risks tied to unplanned drinking among young adults.

5.2. Person-level predictors

The finding that age was associated with increased odds of unplanned drinking challenges the conventional view that impulsive behaviors, such as unplanned drinking, decline with maturity (Steinberg et al., 2008). However, this may reflect the nature of our sample, which consisted of young adults with hazardous alcohol use patterns. Older participants in this group may have more exposure to contexts where alcohol is available or established drinking behaviors that are harder to regulate or modify.

As expected, higher baseline AUDIT-C scores were associated with increased odds of unplanned drinking, aligning with previous research that connects higher alcohol consumption with more frequent unplanned drinking episodes (Fairlie et al., 2019). Surprisingly, college attendance did not significantly predict unplanned drinking or interact with other person-level factors, suggesting that unplanned drinking behaviors are not unique to college students and may be equally prevalent among non-college young adults.

Negative urgency—an impulsivity trait reflecting a tendency to act rashly in response to negative emotions—was a strong predictor of unplanned drinking at the event level. This finding supports prior research linking negative urgency to increased alcohol use and risky drinking behaviors in college students (Anthenien et al., 2017) and provides further evidence of the role of impulsivity in unplanned drinking across broader populations.

The finding that Black participants were more likely than White participants to report unplanned drinking days is novel. It may reflect underlying cultural or social differences in drinking behaviors, possibly related to the experience of racial discrimination. Previous research has shown a link between racial discrimination and increased alcohol consumption in Black young adults (Desalu et al., 2019), raising the possibility that experiences of discrimination could drive spontaneous drinking decisions. Given that these associations were not robust to sensitivity analyses makes confounding or spurious associations possible. Future research should investigate event-level experiences of racial discrimination as potential triggers for unplanned drinking.

5.3. Event-level predictors

The study confirmed the well-established association between weekend days and increased alcohol consumption among young adults (Labhart et al., 2017; Lau-Barraco et al., 2016). The social nature of weekends provides more opportunities for interaction, likely contributing to the increased incidence of unplanned drinking on Fridays and Saturdays. Moreover, the presence of friends—especially friends who are drinking—was a significant predictor of unplanned drinking. This is consistent with Social Cognitive Theory, which posits that peers exert a powerful influence on alcohol consumption in young adults (Borsari & Carey, 2001). Our findings extend these peer-influence effects beyond college populations to a racially and educationally diverse cohort of young adults, suggesting that social environments are a critical context for unplanned drinking regardless of educational background.

5.4. Consequences of unplanned drinking

Our analysis revealed that individuals who frequently engaged in unplanned drinking experienced more negative alcohol-related consequences at the 14-week follow-up, even after accounting for overall alcohol consumption and baseline alcohol-related consequences. This finding contrasts with some prior research suggesting that unplanned drinking may be linked to fewer alcohol-related consequences (Lauher et al., 2020). One possible explanation for this discrepancy is the difference in the comparator. Prior studies often compared unplanned drinking days to planned drinking days, whereas our study examined the cumulative impact of unplanned drinking across individuals over time. The impulsive nature of unplanned drinking likely impairs an individual’s ability to regulate alcohol intake, leading to greater harm, particularly when harm-reduction strategies are not in place.

5.5. Strengths and limitations

A key strength of this study is its use of EMA data, which allowed us to capture day-level drinking behaviors over a 14-week period. This design provided a more nuanced understanding of both person- and event-level predictors of unplanned drinking, offering insights that cross-sectional studies cannot. Moreover, the inclusion of both college and non-college young adults makes our findings more generalizable to a broader population, addressing a significant gap in the literature.

However, the study is not without limitations. First, self-reported alcohol use is inherently subject to recall bias and social desirability effects. Although EMA helps minimize these biases by collecting data in real time, they cannot be entirely eliminated. The social context data collection occurred during the COVID-19 pandemic when social distancing measures were in place. While this might have affected the frequency and nature of social interactions, participants continued to report friend presence during drinking episodes, suggesting that social gathering restrictions may not have substantially altered drinking-related social behaviors in this sample (Suffoletto et al., 2020). Moreover, due to the structure of our EMA data collection, we can only establish that friend contact and drinking occurred on the same day but cannot determine the temporal ordering or physical proximity of these events. This limitation means our findings about social influence should be interpreted as same-day associations rather than immediate situational effects. The universal presence of intervention content means our findings may underestimate the natural occurrence of unplanned drinking in young adults who have not received any intervention. Finally, the study did not capture specific drinking settings (e.g., home vs. bar), which could have provided additional context for understanding unplanned drinking.

5.6. Implications

The findings from this study have several important implications for alcohol interventions targeting young adults. First, the strong association between friend presence and unplanned drinking suggests that interventions should incorporate peer-focused components. This could include teaching young adults to recognize and plan for high-risk social situations, developing strategies for responding to spontaneous drinking invitations, and potentially incorporating supportive peers into intervention delivery. The temporal patterns identified suggest that just-in-time adaptive interventions could be particularly effective if delivered during high-risk periods, such as Friday and Saturday evenings. Given that negative urgency predicted unplanned drinking, interventions might benefit from incorporating emotion regulation skills training, particularly focusing on managing negative emotions without turning to alcohol. The observed relationship between unplanned drinking frequency and negative consequences highlights the importance of incorporating protective behavioral strategies specifically for unplanned drinking situations. This could include pre-commitment strategies (e.g., setting drinking limits before social events), implementation intentions for handling unexpected drinking opportunities, and skills for navigating peer pressure in the moment.

6. Conclusions

Unplanned drinking is a relatively common and risky behavior among young adults, particularly in social contexts and on weekends. Individuals with higher baseline risk factors, such as elevated AUDIT-C scores and higher levels of negative urgency, are more likely to engage in unplanned drinking. The frequency of unplanned drinking episodes, when extreme, was a significant predictor of negative alcohol-related consequences. These findings highlight the need for targeted interventions that focus on reducing unplanned drinking, particularly for high-risk individuals and in environments that encourage spontaneous alcohol use. Interventions that address impulsivity and peer influence may be especially effective in curbing unplanned drinking and its associated harms.

Supplementary Material

1

Highlights.

  • Unplanned drinking occurred on 16.9% of days when participants had no plan to drink.

  • Impulsivity, weekends, and drinking friends were the strongest drivers of unplanned drinking.

  • Frequent unplanned drinking was linked to a 26% higher rate of negative alcohol-related consequences.

Funding:

NIAAA 1R01AA030986-01A1

Statement 1: Role of Funding Sources

BS received funding from the NIAAA which had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

Footnotes

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REFERENCES

  1. Anthenien AM, Lembo J, & Neighbors C (2017). Drinking Motives and Alcohol Outcome Expectancies as Mediators of the Association between Negative Urgency and Alcohol Consumption. Addictive Behaviors, 66, 101–107. 10.1016/j.addbeh.2016.11.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bandura A (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50(2), 248–287. 10.1016/0749-5978(91)90022-L [DOI] [Google Scholar]
  3. Binge Drinking | CDC. (2022, January 10). https://www.cdc.gov/alcohol/fact-sheets/binge-drinking.htm
  4. Borsari B, & Carey KB (2001). Peer influences on college drinking: A review of the research. Journal of Substance Abuse, 13(4), 391–424. 10.1016/s0899-3289(01)00098-0 [DOI] [PubMed] [Google Scholar]
  5. Bradley KA, DeBenedetti AF, Volk RJ, Williams EC, Frank D, & Kivlahan DR (2007). AUDIT-C as a brief screen for alcohol misuse in primary care. Alcoholism, Clinical and Experimental Research, 31(7), 1208–1217. 10.1111/j.1530-0277.2007.00403.x [DOI] [PubMed] [Google Scholar]
  6. Bush K, Kivlahan DR, McDonell MB, Fihn SD, & Bradley KA (1998). The AUDIT alcohol consumption questions (AUDIT-C): An effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Archives of Internal Medicine, 158(16), 1789–1795. 10.1001/archinte.158.16.1789 [DOI] [PubMed] [Google Scholar]
  7. Carter AC, Brandon KO, & Goldman MS (2010). The college and noncollege experience: A review of the factors that influence drinking behavior in young adulthood. Journal of Studies on Alcohol and Drugs, 71(5), 742–750. 10.15288/jsad.2010.71.742 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Coskunpinar A, Dir AL, & Cyders MA (2013). Multidimensionality in Impulsivity and Alcohol Use: A Meta-Analysis using the UPPS Model of Impulsivity. Alcoholism, Clinical and Experimental Research, 37(9), 1441–1450. 10.1111/acer.12131 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cyders MA, Littlefield AK, Coffey S, & Karyadi KA (2014). Examination of a Short Version of the UPPS-P Impulsive Behavior Scale. Addictive Behaviors, 39(9), 1372–1376. 10.1016/j.addbeh.2014.02.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Desalu JM, Goodhines PA, & Park A (2019). Racial discrimination and alcohol use and negative drinking consequences among Black Americans: A meta-analytical review. Addiction (Abingdon, England), 114(6), 957–967. 10.1111/add.14578 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Dvorak RD, Pearson MR, & Day AM (2014). Ecological momentary assessment of acute alcohol use disorder symptoms: Associations with mood, motives, and use on planned drinking days. Experimental and Clinical Psychopharmacology, 22(4), 285–297. 10.1037/a0037157 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Fairlie AM, Cadigan JM, Patrick ME, Larimer ME, & Lee CM (2019). Unplanned Heavy Episodic and High-Intensity Drinking: Daily-Level Associations With Mood, Context, and Negative Consequences. Journal of Studies on Alcohol and Drugs, 80(3), 331–339. 10.15288/jsad.2019.80.331 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Griffin SA, Freeman LK, & Trull TJ (2021). Predictors of unplanned drinking in daily life: The influence of context, impulsivity, and craving in those with emotion dysregulation. Addictive Behaviors, 118, 106901. 10.1016/j.addbeh.2021.106901 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Griffin SA, & Trull TJ (2020). Alcohol use in daily life: Examining the role of trait and state impulsivity facets. Psychology of Addictive Behaviors : Journal of the Society of Psychologists in Addictive Behaviors, 10.1037/adb0000679. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Howard AL, Lamb M, Alexander SM, Bradley AHM, Carnrite KD, Milyavskaya M, Barker ET, & Patrick ME (2024). Planned and unplanned drinking to get drunk: A registered report examining willingness, drinking motives, and protective behavioral strategies using ecological momentary assessment. Psychology of Addictive Behaviors: Journal of the Society of Psychologists in Addictive Behaviors, 38(5), 519–539. 10.1037/adb0000909 [DOI] [PubMed] [Google Scholar]
  16. Kahler CW, Hustad J, Barnett NP, Strong DR, & Borsari B (2008). Validation of the 30-Day Version of the Brief Young Adult Alcohol Consequences Questionnaire For Use in Longitudinal Studies. Journal of Studies on Alcohol and Drugs, 69(4), 611–615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Khadjesari Z, White IR, McCambridge J, Marston L, Wallace P, Godfrey C, & Murray E (2017). Validation of the AUDIT-C in adults seeking help with their drinking online. Addiction Science & Clinical Practice, 12(1), 2. 10.1186/s13722-016-0066-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Lauher ML, Merrill JE, Boyle HK, & Carey KB (2020). The relationship between unplanned drinking and event-level alcohol-related outcomes. Psychology of Addictive Behaviors: Journal of the Society of Psychologists in Addictive Behaviors, 34(4), 497–505. 10.1037/adb0000553 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. McMullin SD, Shields GS, Slavich GM, & Buchanan TW (2021). Cumulative lifetime stress exposure predicts greater impulsivity and addictive behaviors. Journal of Health Psychology, 26(14), 2921–2936. 10.1177/1359105320937055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. NIAAA. (n.d.). Rethinking Dirnking: What is a standard drink? https://rethinkingdrinking.niaaa.nih.gov/how-much-too-much/whats-standard-drink
  21. Pearson MR, & Henson JM (2013). Unplanned Drinking and Alcohol-Related Problems: A Preliminary Test of the Model of Unplanned Drinking Behavior. Psychology of Addictive Behaviors : Journal of the Society of Psychologists in Addictive Behaviors, 27(3), 584–595. 10.1037/a0030901 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Ryu S, & Fan L (2023). The Relationship Between Financial Worries and Psychological Distress Among U.S. Adults. Journal of Family and Economic Issues, 44(1), 16–33. 10.1007/s10834-022-09820-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Stacy AW, & Wiers RW (2010). Implicit cognition and addiction: A tool for explaining paradoxical behavior. Annual Review of Clinical Psychology, 6, 551–575. 10.1146/annurev.clinpsy.121208.131444 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Steinberg L, Albert D, Cauffman E, Banich M, Graham S, & Woolard J (2008). Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: Evidence for a dual systems model. Developmental Psychology, 44(6), 1764–1778. 10.1037/a0012955 [DOI] [PubMed] [Google Scholar]
  25. Substance Abuse and Mental Health Services Administration. (2024). Key substance use and mental health indicators in the United States: Results from the 2023 National Survey on Drug Use and Health (HHS Publication No. PEP24-07-021, NSDUH Series H-59). Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. https://www.samhsa.gov/data/report/2023-nsduh-annual-national-report
  26. Suffoletto B, Callaway C, Kristan J, Kraemer K, & Clark DB (2012). Text-message-based drinking assessments and brief interventions for young adults discharged from the emergency department. Alcoholism, Clinical and Experimental Research, 36(3), 552–560. 10.1111/j.1530-0277.2011.01646.x [DOI] [PubMed] [Google Scholar]
  27. Suffoletto B, Pacella M, Huber J, & Chung T (2022). Effects of text message interventions with different behavior change techniques on alcohol consumption among young adults: A 5-arm randomized controlled trial. Addiction (Abingdon, England). 10.1111/add.16074 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Suffoletto B, Ram N, & Chung T (2020). In-Person Contacts and Their Relationship With Alcohol Consumption Among Young Adults With Hazardous Drinking During a Pandemic. The Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine, 67(5), 671–676. 10.1016/j.jadohealth.2020.08.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Thrul J, & Kuntsche E (2015). The impact of friends on young adults’ drinking over the course of the evening—An event-level analysis. Addiction, 110(4), 619–626. 10.1111/add.12862 [DOI] [PubMed] [Google Scholar]
  30. Watts LL, Hamza EA, Bedewy DA, & Moustafa AA (2024). A meta-analysis study on peer influence and adolescent substance use. Current Psychology, 43(5), 3866–3881. 10.1007/s12144-023-04944-z [DOI] [Google Scholar]

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