Abstract
Background and aims
Harmful alcohol consumption remains widespread among university students and is associated with numerous academic, social and health‐related consequences. Peer‐led approaches offer a promising strategy to reduce risky drinking in this population. This study evaluated the long‐term effects of a peer‐led Brief Alcohol Screening and Intervention for College Students (BASICS) program.
Design
A parallel two‐group randomised controlled trial with a follow‐up period of 12 months was conducted.
Setting
The study took place at a university in Spain.
Participants
A total of 308 undergraduate students who had engaged in at least one episode of heavy drinking in the previous month were randomly assigned to either the intervention group (n = 154) or the control group (n = 154).
Intervention
The intervention group received a single individual session of the peer‐delivered BASICS program. The control group received no intervention during the study period.
Measurements
The primary outcome was the number of alcoholic drinks consumed during a typical week at the 12‐month follow‐up. Secondary outcomes included weekly drinking at one month, as well as weekend drinking, drinks on the heaviest occasion, binge drinking episodes, peak blood alcohol concentration during a typical week and on the heaviest occasion, alcohol‐related consequences, motivation to reduce alcohol use and self‐efficacy, all of which were assessed at both 1 and 12 months.
Findings
At the primary 12‐month follow‐up, statistically significant between‐group differences were observed for all nine measured outcomes. For the primary outcome, the mean weekly alcohol consumption was 8.93 drinks [95% confidence interval (CI) = 7.7–10.2] in the intervention group and 12.35 drinks (95% CI = 11.1–13.6) in the control group. The between‐group difference was 3.42 drinks (95% CI = 1.66–5.18), indicating lower consumption in the intervention group. Among the secondary outcomes, statistically significant differences were also observed for weekend drinking (3.08 drinks, 95% CI = 1.59–4.56), drinks on the heaviest occasion (3.27 drinks, 95% CI = 1.79–4.76), binge drinking frequency (0.93 episodes, 95% CI = 0.54–1.32) and alcohol‐related consequences (3.28 points, 95% CI = 1.74–4.82).
Conclusions
Among Spanish college students who had engaged in at least one episode of heavy drinking in the previous month, a single peer‐led Brief Alcohol Screening and Intervention for College Students (BASICS) session produced sustained improvements in alcohol use, related consequences, and psychological mediators up to 12 months post‐intervention.
Keywords: alcohol use, BASICS, brief motivational intervention, harm reduction, long‐term effects, peer‐led intervention, randomised controlled trial, university students
INTRODUCTION
Since the Ottawa Charter in 1986, health promotion has been a key strategy for addressing the epidemic of non‐communicable diseases (NCDs), with the development of the individual’s personal skills identified as one of its most promising strategic areas [1]. Despite ongoing efforts in education and prevention, risky lifestyle behaviours remain widespread globally, driving many chronic illnesses and mental health conditions [2]. Among harmful substances, alcohol is the most widely used, contributing substantially to the global burden of disease, disability and mortality [3].
University environments are key settings in which alcohol consumption is deeply embedded in social norms, often normalising and even encouraging excessive drinking [4]. This cultural acceptance contributes to high rates of hazardous alcohol use among students. Across Europe, approximately 30% of university students are classified as hazardous drinkers [5]. In Spain, the statistics are even more concerning: recent studies indicate that approximately 40% of students engage in risky alcohol consumption, and approximately 27% report regular episodes of binge drinking [6]. These patterns have been linked to adverse outcomes, including accidents, injuries, academic difficulties, impaired driving and interpersonal violence [7, 8]. Although not all students develop alcohol use disorders, repeated binge drinking during this life stage may negatively impact long‐term health and wellbeing [9, 10]. Given that the university years are critical for consolidating lifelong habits, this setting offers a key opportunity for implementing targeted and effective alcohol prevention strategies.
Over the past three decades, the Brief Alcohol Screening and Intervention for College Students (BASICS) has become among the most extensively studied and disseminated interventions for reducing risky drinking among university students [11, 12, 13]. Meta‐analytic evidence indicates that compared with control conditions, BASICS and similar brief interventions typically reduce alcohol consumption by approximately 2–6 standard drinks per week, with small‐to‐moderate effect sizes (Cohen’s d ≈ 0.30–0.60) [14, 15]. These effects have been replicated across voluntary and mandated student samples and are maintained, although attenuated, up to 12 months after the intervention.
Several studies have also explored the effectiveness of peer‐led versions of BASICS, aiming to increase its feasibility and cost‐effectiveness in university settings. Evidence suggests that when properly trained and supervised, peer counsellors can achieve outcomes comparable with those delivered by professional staff. Mastroleo et al. [16] reported that compared with control students, students who voluntarily engaged in a peer‐led BASICS session significantly reduced their alcohol consumption by 4.81 drinks per week at the 3‐month follow‐up. Consistent with these findings, a recent scoping review of 13 peer‐led studies [12] reported follow‐up periods ranging from 3 to 12 months, with only two studies including 12‐month assessments, with both evaluating BASICS‐type interventions, and showing average reductions of approximately 3–6 drinks per week compared with control conditions.
Most of these studies were conducted more than a decade ago [14, 17, 18], and to date no study has assessed the long‐term effects of BASICS in the Spanish university context. As alcohol use is not static but rather influenced by evolving social environments, academic pressures and personal stressors throughout their university years [19, 20], it is crucial to explore whether the initial reductions in alcohol achieved through BASICS in the short term are maintained over time. This study therefore evaluated the 12‐month effects of a peer‐led BASICS intervention among Spanish university students.
METHODS
Study design
This study employed a parallel two‐group randomised controlled trial (RCT) to evaluate the long‐term effects of a peer‐led intervention aimed at reducing alcohol consumption and related consequences among university students. The trial followed a two‐arm parallel design with follow‐up assessments conducted at 1 and 12 months post‐intervention. The intervention consisted of a single session and was compared with a control group that received no intervention. The trial protocol was registered at ClinicalTrials.gov (NCT05639374).
Participants
The study sample consisted of 308 undergraduate students from a private university in Spain who were recruited between November 2022 and January 2023 from different faculties. Eligible participants were those in their first or second year of study, aged 18 years or older and who had engaged in at least one episode of heavy drinking in the previous month. Students who had previously completed similar alcohol education programs were excluded from the study. Recruitment was conducted through announcements in university classrooms and student residences, as well as through promotional content shared on the university’s social media platforms and student newsletter.
Procedures
All participants provided informed consent before enrolment. After completing the online baseline assessment through the SurveyMonkey platform, participants were randomly assigned to either the intervention group or the control group using a simple computer‐generated random sequence created by an independent statistician using Stata 15 (StataCorp LLC, College Station, TX, USA).
Group allocation was concealed from the research team until randomisation was completed, which effectively functioned as a centralised allocation process to prevent foreknowledge of group assignment.
Once the randomisation was finalised, participants in the intervention group received an email from the study account (not from the institutional email of the research team) with a calendar to select the most convenient date and time for their intervention session. Communication with participants was conducted exclusively through the study email address to maintain confidentiality and minimise bias.
Participants completed the same online questionnaire via SurveyMonkey at both follow‐ups, which included alcohol‐related outcome measures. For the control group, assessments took place 1 and 12 months after the baseline assessment, whereas for the intervention group, these assessments occurred 1 and 12 months after receiving the intervention. If participants did not respond, they were sent two reminders. The person responsible for sending follow‐up questionnaires was blinded to group allocation. Only the peer counsellors who delivered the intervention were aware of the participant’s group assignment, as they conducted the intervention sessions.
Intervention
The intervention implemented in this study was a peer‐led version of the BASICS programme, designed to reduce alcohol consumption and its negative consequences among university students. The BASICS protocol and feedback materials were translated into Spanish and culturally adapted to the university context. The adaptation process involved linguistic translation, expert review by Spanish‐speaking professionals with experience in alcohol prevention and minor contextual adjustments to ensure cultural relevance (e.g. local drinking norms and terminology). A pilot test was conducted with a small group of students to evaluate clarity, acceptability and cultural appropriateness, and minor refinements were made accordingly.
The intervention consisted of a single, structured, face‐to‐face session lasting approximately 50 minutes; it was conducted individually in a private room at the university, ensuring confidentiality. The session followed the principles of motivational interviewing and was guided by a personalised feedback sheet generated from each participant’s baseline assessment data. This feedback included personalised data on alcohol consumption, estimated blood alcohol levels, normative comparisons, and educational content addressing common myths and risks associated with alcohol use.
The intervention was delivered by nine fourth‐year nursing students trained as peer facilitators. They were trained following a programme that included 12 hours of theoretical instruction on alcohol‐related risks, motivational interviewing and harm reduction strategies, and 10 hours of practical sessions involving role plays and supervised practice with volunteer students acting as intervention recipients, under faculty supervision. To be eligible to participate in the study as peer counsellors, students were required to demonstrate their knowledge through a written exam and their practical skills by recording two simulated motivational interviewing sessions focused on alcohol use. Facilitators followed a standardised intervention manual to ensure consistency in delivery.
Participants in the control group did not receive any intervention during the study period. After the trial was completed, they were offered the opportunity to receive the intervention.
No modifications were made to the intervention during the course of the study. Although fidelity to the protocol was not formally measured, the structured training and use of detailed session guides were designed to promote adherence. Additional information can be found in the corresponding reference [21].
Measurements
Data were collected at three time points—at baseline and then 1 and 12 months post‐intervention—using a self‐report questionnaire validated for the Spanish university population. This questionnaire included 25 questions divided into two sections: socio‐demographic variables (first eight questions) and 17 questions about participant’s history of alcohol consumption and alcohol‐related consequences during the previous month.
Primary outcome measure
The primary outcome was alcohol consumption during a typical week at the 12‐month follow‐up, measured using the Daily Drinking Questionnaire–Revised (DDQ‐R) [22]. Participants reported the number and type of alcoholic beverages typically consumed (wine, beer and spirits), as well as the approximate quantity in millilitres for each type. These values were then converted into standard drink units (SDUs), defined as 10 g of pure alcohol, following Spanish national guidelines. The total weekly consumption was calculated by summing the SDUs across all reported beverage types.
Secondary outcome measures
Secondary outcomes included weekly alcohol consumption at 1 month, which was assessed using the same procedure as for the primary outcome (DDQ‐R) [22]. Additional secondary outcomes were: (i) the average number of alcoholic drinks consumed during a typical weekend; (ii) the number of drinks consumed on the heaviest drinking occasion, both of which were measured with the DDQ‐R; (iii) the estimated blood alcohol concentration (BAC) during a typical week; (iv) the peak BAC on the heaviest drinking occasion, both of which were calculated using the quantity/frequency/peak (QF) index [23] and expressed in grams per litre (g/L); (v) binge‐drinking frequency, which was assessed through a closed‐ended question; (vi) motivation to reduce alcohol consumption; (vii) self‐efficacy to do so, which was measured using a 10‐point Likert scale; and (viii) alcohol‐related consequences, which were evaluated using the Spanish version of the Young Adult Alcohol Consequences Questionnaire (S‐YAACQ) [24].
All secondary outcomes were assessed at both the 1‐month and 12‐month follow‐ups, unless specified otherwise.
Other measures
Baseline socio‐demographic and behavioural variables were collected to describe the sample and to generate the personalised feedback sheet used in the intervention. These included: age, sex, nationality, place of residence, weight, physical activity, current medication use, whether participants had previously started or completed another university degree or higher education programme and whether they held a driving licence.
Baseline alcohol‐related measures included age of drinking onset, average amount of money spent on alcohol in a typical week and the Alcohol Use Disorders Identification Test–Consumption (AUDIT‐C) score [25]. Participants were also asked whether, during the previous month, they had: (i) driven after drinking alcohol; or (ii) been a passenger in a car driven by someone who had been drinking. Finally, two perception items were used to assess participants’ beliefs about peer drinking norms, asking what percentage of university students they believed to be abstainers and what percentage they believed to engage in binge drinking (a definition was provided in the questionnaire).
Statistical methods
Sample size calculation
For the sample size calculation, the trial was powered for a two‐group comparison of mean weekly alcohol consumption at 12 months using a two‐sample Student’s t‐test (two‐sided α = 0.05; 80% power). Based on the observed 12‐month means in a prior BASICS randomised controlled trial (8.94 ± 6.40 vs 11.94 ± 4.59 drinks/week), we assumed a between‐group difference of 3.0 drinks per week and a common standard deviation of 5.6 drinks per week (Cohen’s d ≈ 0.54) [26]. Under these assumptions, 55 participants per group (110 in total) were needed. In anticipation of approximately 30% attrition at the 12‐month follow‐up, consistent with previous BASICS retention rates, we set the total target sample size to 160 participants (80 per group) to ensure adequate statistical power [26].
Descriptive analyses
Baseline data for continuous variables are presented as means and 95% confidence intervals (95% CIs), as all variables were approximately normally distributed. Categorical data are expressed as frequencies and percentages (n, %).
Primary and secondary outcome analyses
To test the intervention effect over time, linear mixed‐effects models were fitted with time (baseline, 1 month, 12 months) as the within‐subject factor and group (intervention vs control) as the between‐subject factor. Models included a random intercept for participants to account for within‐subject correlations across repeated measures. The time × group interaction tested whether the changes in outcomes differed between groups across time.
Model assumptions and sensitivity analyses
Model assumptions were verified through visual inspection of residual plots, histograms, box plots and quantile–quantile (Q–Q) plots, and Shapiro–Wilk tests of normality were performed. Although the Shapiro–Wilk tests indicated statistically significant deviations from normality for some outcomes (particularly those based on count data, such as binge‐drinking episodes and drinks on the heaviest occasion), the corresponding W‐values were close to 1, and visual inspection suggested that residuals were approximately normally distributed without substantial skewness or kurtosis (see the Supporting information, Table S2 and Figures S5–S10). Given this and the robustness of linear mixed‐effects models to modest non‐normality in moderate‐to‐large samples (n > 300), the use of parametric models was considered appropriate.
To assess the robustness of findings, adjusted mixed‐effects models were estimated controlling for relevant socio‐demographic variables (age, sex, faculty, type of residence, sports practice and weight).
Missing data
Missing data were handled using a full information maximum likelihood (FIML) approach within the mixed‐effects models, which incorporates all available data without case‐wise deletion. Analyses assumed that the data were missing at random (MAR), meaning that the probability of missingness depended only on observed characteristics (e.g. baseline variables or group allocation) but not on unobserved outcomes.
This assumption was supported by the very high retention rate (97.7%), the complete follow‐up at 1 month, and the absence of baseline differences between participants who completed the 12‐month assessment and those who did not complete it. These patterns suggest that attrition was related to observable factors (e.g. age, sex, faculty, baseline alcohol use) rather than alcohol‐related outcomes, making the MAR a reasonable and empirically supported assumption.
Clustering effects
As the intervention was delivered individually by nine trained peer counsellors, potential clustering effects by counsellors were evaluated for the primary outcome (weekly alcohol consumption at 12 months). The intra‐class correlation coefficient (ICC) was 0.004 (95% CI = 0.000–0.027), indicating negligible between‐counsellor variability. Therefore, no adjustment for clustering was applied in the analyses.
Software
All the statistical analyses were performed using Stata 15, with significance set at P < 0.05.
Ethical considerations
The study received ethical approval from the university’s ethics committee (code: 2021.162). Informed consent was obtained from all participants before they participated in the study. To protect participant confidentiality, data were codified and students were informed of their right to withdraw from the study at any time without consequences.
RESULTS
Participants
The study participant flow is shown in Figure 1. Among the 2780 students invited, 384 agreed to participate. Among these, 308 students were eligible and randomised between the intervention group (n = 154) and the control group (n = 154). All participants (100%) completed the 1‐month follow‐up assessment. By the 12‐month mark, 301 students (97.7%) had completed the follow‐up questionnaire, including 150 from the intervention group and 151 from the control group.
FIGURE 1.
Consolidated Standards of Reporting Trials (CONSORT) 2025 flow diagram.
Demographic characteristics
The demographic characteristics of both groups are presented in Table 1. The average age of the participants was 18.75 years (SD = 0.7 years), with the majority being female (67.5%). In terms of living arrangements, 33.4% of the students lived with their families, 43.2% resided in student dormitories or colleges and 23.4% lived in shared student apartments. With respect to academic disciplines, 40.9% were in bioscience, 48.0% were in social sciences and 11.0% were in engineering or architecture. Additionally, 60.7% of the participants were first‐year students, while 39.3% were in their second year.
TABLE 1.
Baseline characteristics of participants by groups.
| Control group | Intervention group | |
|---|---|---|
| (n = 154) | (n = 154) | |
| Demographic characteristics | ||
| Age, years, mean (95% CI) | 18.7 (18.6–18.8) | 18.8 (18.6–18.9) |
| Weight, kg, mean (95% CI) | 63.4 (61.6–65.1) | 63.8 (62.0–65.6) |
| Sex, n (%) | ||
| Male | 52 (33.8%) | 48 (31.2%) |
| Female | 102 (66.2%) | 106 (68.8%) |
| Provenance, n (%) | ||
| Spanish | 110 (71.4%) | 121 (78.6%) |
| South American | 26 (16.9%) | 17 (11.0%) |
| North American | 12 (7.8%) | 8 (5.2%) |
| Central America | 2 (1.3%) | 5 (3.2%) |
| Others (Italian, Hungarian) | 4 (2.6%) | 3 (2.0%) |
| Residence, n (%) | ||
| Family home | 49 (31.8%) | 54 (35.1%) |
| Student residence | 67 (43.5%) | 67 (43.5%) |
| Student flat | 38 (24.7%) | 33 (21.4%) |
| Faculty, n (%) | ||
| Bio sanitary sciences | 60 (39.0%) | 61 (39.6%) |
| Social sciences | 78 (50.6%) | 75 (48.7%) |
| Engineering and architecture | 16 (10.4%) | 18 (11.7%) |
| Academic year, n (%) | ||
| First year | 90 (58.4%) | 97 (63.0%) |
| Second year | 64 (41.6%) | 57 (37.0%) |
| Practice sport, n (%) | ||
| No | 56 (36.4%) | 49 (31.8%) |
| Yes | 98 (63.6%) | 105 (68.2%) |
| Age start drinking, years, mean (95% CI) | 15.5 (15.2–15.7) | 15.4 (15.2–15.6) |
| Drinking variables | ||
| Drinks per week, mean (95% CI) | 12.4 (11.0–13.8) | 12.6 (11.2–14.0) |
| Drinks per weekend, mean (95% CI) | 10.3 (9.2–11.5) | 10.4 (9.2–11.6) |
| Drinks on peak occasion, mean (95% CI) | 11.9 (10.9–13.0) | 12.2 (11.2–13.2) |
| Binge‐drinking episodes, mean (95% CI) | 2.2 (2.0–2.5) | 2.1 (1.9–2.4) |
| Peak BAC (g/L) on a typical week, mean (95% CI) | 0.14 (0.13–0.16) | 0.14 (0.13–0.16) |
| Peak BAC (g/L) on peak occasion, mean (95% CI) | 0.22 (0.21–0.24) | 0.23 (0.21–0.25) |
| Alcohol‐related consequences, mean (95% CI) | 10.2 (9.1–11.3) | 10.1 (9.0–11.2) |
| Motivation to change alcohol use, mean (95% CI) | 3.1 (2.6–3.5) | 2.9 (2.5–3.3) |
| Self‐efficacy, mean (95% CI) | 8.0 (7.7–8.3) | 7.8 (7.5–8.1) |
Note: BAC = blood alcohol concentration.
At baseline, participants reported an average of 12.5 drinks per week and 10.4 drinks during a typical weekend. On their heaviest drinking occasion, they consumed approximately 12.1 drinks, with an average of 2.2 binge‐drinking episodes reported in the past month. The peak estimated BAC during a typical week was 0.14 g/L, while the peak BAC on the heaviest drinking occasion reached 0.23 g/L. The participants experienced an average of 10.2 alcohol‐related consequences. The baseline motivation to change alcohol use was moderate (mean = 3.0), whereas the self‐efficacy levels were relatively high (mean = 7.9).
Primary outcomes
At the 12‐month follow‐up, compared with the control group, the intervention group reported significantly lower alcohol consumption during a typical week. Specifically, the intervention group consumed an average of 8.93 drinks per week, while the control group consumed an average of 12.35 drinks, resulting in a mean difference of 3.42 drinks (95% CI 1.66–5.18 drinks) (Table 2).
TABLE 2.
Mean (95% CI) values for primary and secondary outcomes by group and time point (baseline and 1‐month and 12‐month follow‐ups)
| Outcome | 1‐Month CG | 1‐Month IG | Between‐group difference | P | 12‐Month CG | 12‐Month IG | Between‐group difference | P |
|---|---|---|---|---|---|---|---|---|
| Mean (95% CI) | Mean (95% CI) | Mean (95% CI) | Mean (95% CI) | Mean (95% CI) | Mean (95% CI) | |||
| Drinks per week | 12.3 (11.0–13.7) | 6.6 (5.2–7.9) | 5.7 (5.5, 5.9) | <0.001 | 12.35 (11.1–13.6) | 8.93 (7.7–10.2) | 3.42 (1.66, 5.18) | <0.001 |
| Drinks per weekend | 10.6 (9.4–11.8) | 5.2 (4.1–6.4) | 5.4 (3.6, 6.7) | <0.001 | 10.46 (9.3–11.6) | 7.38 (6.4–8.4) | 3.08 (1.59, 4.56) | <0.001 |
| Drinks on peak occasion | 11.8 (10.8–12.9) | 6.9 (5.9–7.9) | 4.9 (4.8, 5.0) | <0.001 | 12.65 (11.6–13.7) | 9.38 (8.3–10.5) | 3.27 (1.79, 4.76) | <0.001 |
| Binge‐drinking episodes | 2.3 (2.0–2.6) | 0.9 (0.6–1.2) | 1.4 (1.4, 1.4) | <0.001 | 2.53 (2.3–2.8) | 1.60 (1.3–1.9) | 0.93 (0.54, 1.32) | <0.001 |
| Peak BAC (g/L) on a typical week | 0.14 (0.13–0.16) | 0.08 (0.06–0.09) | 0.06 (0.05, 0.07) | <0.001 | 0.15 (0.13–0.16) | 0.09 (0.08–0.11) | 0.05 (0.04, 0.07) | <0.001 |
| Peak BAC (g/L) on a peak occasion | 0.22 (0.21–0.24) | 0.13 (0.110.15) | 0.09 (0.08, 0.10) | <0.001 | 0.22 (0.21–0.24) | 0.15 (0.13–0.17) | 0.07 (0.05, 0.1) | <0.001 |
| Alcohol‐related consequences | 10.2 (9.1–11.3) | 4.4 (3.3–5.5) | 5.8 (5.6, 5.9) | <0.001 | 11.17 (10.1–12.3) | 7.89 (6.7–9.1) | 3.28 (1.74, 4.82) | <0.001 |
| Motivation to change | 3.2 (2.7–3.6) | 4.0 (3.5–4.4) | –0.8 (–0.8, –0.7) | <0.001 | 3.15 (2.8–3.5) | 5.98 (5.6–6.4) | –2.83 (–3.37, –2.28) | <0.001 |
| Self‐efficacy | 7.9 (7.6–8.2) | 8.1 (7.9–8.5) | –0.2 (–0.2, –0.1) | =0.274 | 6.91 (6.6–7.2) | 9.15 (9.0–9.3) | –2.24 (–2.55, –1.93) | <0.001 |
Note: BAC = blood alcohol concentration; CG = control group; IG = intervention group. P‐values represent between‐group comparisons at each time point.
Secondary outcomes
As shown in Table 2, regarding secondary outcomes, students in the intervention group reported significantly lower weekly alcohol consumption at 1 month than the control group.
At both follow‐ups, the intervention group exhibited lower weekend drinking, fewer binge‐drinking episodes, lower estimated BAC levels and fewer alcohol‐related consequences than the control group did. The motivation to reduce alcohol use and self‐efficacy was also higher among the intervention participants, with improvements that were maintained over time.
Specifically, at 12 months, the students in the intervention group consumed significantly fewer drinks during a typical weekend, with a mean difference of 3.08 drinks (95% CI = 1.59–4.56 drinks). With respect to the number of drinks consumed on the occasion of greatest consumption, the intervention group showed a mean difference of 3.27 drinks (95% CI = 1.79–4.76 drinks). The number of binge‐drinking episodes was also significantly lower among the intervention students, with a mean difference of 0.93 episodes (95% CI = 0.54–1.32 episodes).
The peak BAC during a typical week was reduced in the intervention group, with a mean difference of 0.05 g/L (95% CI = 0.04–0.07 g/L). The peak BAC on the occasion of greatest consumption was also significantly lower, with a mean difference of 0.07 g/L (95% CI = 0.05–0.1 g/L). The number of alcohol‐related consequences was significantly lower among students in the intervention group, with a mean difference of 3.28 consequences (95% CI = 1.74–4.82).
The motivation to change alcohol use was significantly greater in the intervention group, with a mean difference of 2.83 points (95% CI = 2.28–3.37). Self‐efficacy was also significantly greater among students who received the intervention, with a mean difference of 2.24 points (95% CI = 1.93–2.55 points).
Figure 2 illustrates the changes in all primary and secondary outcomes across the three time points (baseline and 1‐month and 12‐month follow‐ups) for both groups. The graphical trends support the statistical findings, highlighting the sustained effects of the intervention over time.
FIGURE 2.
Changes in primary and secondary outcomes over time by study group (baseline, 1‐month, and 12‐month follow‐up). BAC = blood alcohol concentration.
Adjusted mixed‐effects models controlling for age, sex, faculty, type of residence, sports practice and weight produced estimates comparable with those obtained in the primary unadjusted analyses (see Supporting information, Figures S1–S10 and Tables S1–S2).
DISCUSSION
This randomised controlled trial evaluated the long‐term effectiveness of a peer‐led BASICS intervention among Spanish university students. While the reductions in alcohol use and related harm observed at 1 month post‐intervention attenuated over time, the intervention group continued to exhibit significantly better outcomes than the control group did at 12 months. Specifically, sustained decreases were noted in weekly and weekend alcohol consumption, peak alcohol consumption, binge alcohol consumption, estimated peak BAC and alcohol‐related consequences. Notably, while improvements in motivation to change were maintained, significant gains in self‐efficacy emerged only at the 12‐month follow‐up, suggesting a delayed consolidation of behavioural change. These findings indicate that peer‐led BASICS interventions can produce enduring effects on alcohol‐related behaviours in university populations, potentially extending benefits well beyond the immediate post‐intervention period.
While most previous evaluations of BASICS interventions have focused on short‐term outcomes (typically up to 6 months), this study contributes valuable evidence on the long‐term maintenance of effects. Several mechanisms may explain the sustained effectiveness of the peer‐led BASICS intervention. First, the intervention was grounded in motivational interviewing (MI), a technique known to support behaviour change through empathy, reflective listening and a collaborative goal‐setting approach. Peer facilitators received structured MI training and demonstrated strong skills in delivering its core components, which likely enhanced participants’ readiness to change and reduced resistance to the intervention messages [16, 18].
Second, the fact that the intervention was delivered by a peer counsellor may have played a central role in its effectiveness. University students are particularly susceptible to social norms and peer behaviours, which strongly shape their attitudes and actions regarding alcohol use. Interventions delivered by trained student peers, who share similar developmental stages, experiences and environments, are often perceived as more credible and relatable [27, 28, 29]. This shared identity can enhance trust, increase openness and receptiveness, and ultimately strengthen the impact of the intervention. Similar dynamics have been observed in other peer‐led interventions within university settings, which highlight that social proximity and having a common background are linked to greater emotional wellbeing, social integration and message acceptance, even beyond the scope of alcohol prevention [30]. These findings suggest that peer‐led models are particularly well suited to influence behaviour in this population, given their sensitivity to peer approval and group belonging.
Third, the personalised feedback provided during the BASICS session may have contributed to the outcomes observed. Individualised information on drinking patterns and associated risks has been shown to increase self‐awareness and motivation [31, 32, 33]. This feedback, delivered in a non‐judgmental and collaborative format, may have catalysed critical reflection and promoted goal‐directed behaviour change. In addition to normative comparisons, the feedback also included educational information aimed at correcting common myths and misconceptions about alcohol, which may have further enhanced its impact. By comparing students’ self‐reported drinking with peer‐based normative data, the intervention increased self‐awareness and helped students recognise discrepancies between their own behaviours and actual peer norms [32, 33, 34]. Providing normative information also corrected overestimations regarding the prevalence and acceptability of heavy drinking among peers [32, 35], which is a well‐documented driver of excessive alcohol use.
These findings can also be interpreted in light of previous meta‐analytic data on BASICS. In a systematic review of 18 randomised controlled trials evaluating face‐to‐face BASICS interventions, Fachini et al. [14] reported a pooled mean reduction of 1.50 drinks per week (95% CI = 0.29–3.24 drinks) and a decrease of 0.87 points in alcohol‐related problems (95% CI = 0.20–1.58 points) at the 12‐month follow‐up. Compared with these pooled estimates, the present study observed larger reductions in weekly alcohol consumption and alcohol‐related consequences. Although the studies included by Fachini et al. [14] focused on voluntary samples of heavy‐drinking students, there was heterogeneity in study design, recruitment context and intervention delivery. Differences in participant engagement and implementation characteristics (particularly the peer‐led and face‐to‐face nature of the present intervention) may partly explain the larger effect sizes observed. Moreover, voluntary participation in behavioural interventions is widely recognised as a source of recruitment or self‐selection bias, as individuals who choose to take part in research may differ systematically from the broader target population in motivation, engagement and access to resources [36]. Overall, these findings suggest that a peer‐led version of BASICS can produce effects that are at least comparable with those reported in previous trials.
Our findings show a modest decline in effect size between the 1‐ and 12‐month follow‐ups, which is expected in behavioural interventions. Nonetheless, the sustained differences observed at 12 months remain both meaningful and clinically relevant, highlighting the lasting impact of the intervention. One potential explanation for the observed decline is the absence of reinforcement mechanisms during the follow‐up period. Previous research has demonstrated that reminder systems, booster sessions or repeated exposure to intervention content can help maintain behaviour change [37, 38]. While digital interventions often include such mechanisms, in‐person programmes such as BASICS typically do not. Adding low‐cost reinforcement strategies, such as periodic emails or peer check‐ins, could help sustain or even enhance long‐term outcomes. Indeed, Braitman and Lau Barraco [39] reported that personalised email boosters following a computerised intervention produced significant reductions in drinking among legal‐age college students. Similar reinforcement approaches are currently being evaluated in fraternity and sorority students in the USA through the Project Greek trial [40].
In addition to behavioural outcomes, the intervention resulted in significant long‐term improvements in the students’ motivation to change their drinking habits. While both groups started with similar baseline levels, only the intervention group showed a progressive and statistically significant increase in motivation at both 1 and 12 months. These findings may be partially explained by the motivational interviewing approach, which aims to enhance intrinsic motivation. As students reduce their alcohol intake, they may experience fewer negative consequences, such as injuries, interpersonal conflicts or academic difficulties, which can reinforce their desire to maintain lower levels of consumption [41, 42]. Moreover, reduced alcohol use has been associated with greater life satisfaction and improved overall wellbeing among university students [43]. The peer‐led nature of the intervention may have further increased its effectiveness by enhancing the credibility and relatability of the message, fostering a stronger connection with participants [44]. Emphasizing the negative consequences of excessive alcohol use, including health risks and academic problems, may also have contributed to strengthening the students; motivation to change [45].
A similar pattern was observed for self‐efficacy, which did not differ between groups at baseline or at 1 month but showed a substantial and significant increase in the intervention group by 12 months. Previous studies have identified self‐efficacy as both a key mechanism and an important outcome in alcohol reduction interventions [46], reinforcing the significance of our findings. One possible explanation for this improvement is that self‐efficacy requires time to develop, as it is often built through mastery experiences and repeated opportunities to apply new behaviours in real‐life contexts [47, 48]. During the months following the intervention, students in the BASICS group may have had multiple chances to successfully manage drinking‐related situations, thereby strengthening their belief in their own ability to control alcohol use. This gradual consolidation aligns with behaviour change literature, which shows that delayed improvements in self‐efficacy are common and typically emerge after initial increases in motivation and behavioural experimentation [38, 49].
In interpreting these findings, the recruitment process in this study highlights two well‐known realities within university‐based alcohol prevention research: the normalisation of drinking behaviours and the voluntary nature of participation, both of which tend to limit student engagement in preventive programmes. Previous studies have shown that many university students perceive alcohol use as a normative and even expected aspect of campus life, which diminishes their motivation to participate in interventions addressing this behaviour [4, 50]. These factors likely contributed to the modest initial uptake observed here (13.8%; 384 of 2780 invited). Moreover, the in‐person delivery format may have introduced additional barriers, such as scheduling conflicts and the need to attend sessions on campus, challenges that have also been reported in previous BASICS and peer‐led intervention studies [51, 52]. Similar recruitment and engagement difficulties have been described in recent prevention trials among university students [51, 53], underscoring that low participation rates are a common feature of real‐world alcohol prevention research in this population.
Several limitations should be acknowledged. First, the study relied on self‐reported measures of alcohol use, which may be subject to recall or social desirability bias. Second, although the participants were randomised, the sample was drawn from a single university context, which may limit the generalisability of the findings; future studies should assess the effectiveness of this intervention in other university settings and cultural contexts. Third, although the communication strategy was designed to reach nearly all first‐ and second‐year university students (approximately 2780 individuals), only 384 students were enrolled in the study. This corresponds to a relatively low participation rate. Participation was entirely voluntary and focused on alcohol‐related behaviours, which may have attracted students with greater interest in health promotion or behaviour change. While this participation pattern may introduce a degree of self‐selection bias, it also reflects the practical challenges of engaging students in voluntary health‐promotion initiatives. Importantly, comparisons of socio‐demographic characteristics (age, sex and academic discipline) indicated that participants were broadly comparable with the overall student population, suggesting reasonable representativeness despite potential self‐selection bias. Finally, while the 12‐month follow‐up represents a significant advance over prior BASICS studies, longer‐term assessments are needed to determine whether the benefits are maintained beyond the first academic year.
Clinical and public health implications
These findings highlight the potential of peer‐led interventions as a scalable approach to reduce alcohol consumption and related harm in university settings. By leveraging the credibility and perceived similarity of peers, this model can reach students who might be less receptive to traditional health services. Embedding such programs within campus infrastructures or student health and wellbeing units may enhance their reach and sustainability, particularly in environments with limited access to professional services. The sustained reductions in alcohol use and related consequences suggest that even brief, peer‐delivered sessions can contribute meaningfully to early prevention and harm reduction strategies.
Beyond their clinical value, these interventions also align with broader health promotion frameworks that emphasise action outside traditional health care settings. In line with the Ottawa Charter, which underscores the importance of enabling individuals and communities to take control over their health, universities represent key environments for fostering health‐promoting behaviours. In this context, trained students act as health assets, proactively supporting their peers and reinforcing positive social norms. Their involvement expands the reach of prevention efforts and helps to develop supportive environments for long‐term behaviour change.
Future research directions
Future research should examine the effectiveness of peer‐led BASICS interventions across more diverse educational and cultural contexts to improve the generalisability. Longitudinal studies that assess outcomes over several years would help determine the durability of behavioural changes and their impact into adulthood. Additionally, hybrid models that combine the motivational strengths of face‐to‐face peer support with the accessibility and reinforcement potential of digital tools, such as apps, SMS reminders or online modules, should be explored. Such integrative approaches may enhance both engagement and sustainability, offering a promising pathway for health promotion in higher education.
CONCLUSION
This study provides evidence for the long‐term effectiveness of a peer‐led, face‐to‐face, one‐session BASICS intervention in reducing alcohol use and related harm among university students. Beyond immediate post‐intervention effects, the program contributed to sustained improvements in both behavioural and psychological outcomes, including motivation and self‐efficacy. By integrating the core elements of motivational interviewing, peer facilitation and personalised normative feedback, the intervention addresses multiple determinants of alcohol‐related behaviour. These findings highlight the relevance of low‐intensity, peer‐delivered strategies as a complementary approach to traditional services in higher education settings and suggest that such models may hold potential for broader implementation in university‐based alcohol prevention efforts.
AUTHOR CONTRIBUTIONS
María Lavilla‐Gracia: Conceptualization (equal); data curation (equal); formal analysis (equal); funding acquisition (equal); investigation (equal); methodology (equal); writing—original draft (equal). María Pueyo‐Garrigues: Conceptualization (equal); funding acquisition (equal); investigation (equal); methodology (equal); supervision (equal); writing—review and editing (equal). Cristina Alfaro‐Díaz: Conceptualization (equal); writing—review and editing (equal). Navidad Canga‐Armayor: Conceptualization (equal); funding acquisition (equal); investigation (equal); methodology (equal); supervision (equal); writing—review and editing (equal).
DECLARATION OF INTERESTS
The authors declare that they have no conflicts of interest.
CLINICAL TRIAL REGISTRATION DETAILS
The trial protocol was registered at ClinicalTrials.gov (NCT05639374) https://clinicaltrials.gov/study/NCT05639374.
Supporting information
Table S1. Descriptive statistics (mean, SD, median, p25, p75, min, max, N) for count outcomes (binge drinking episodes and drinks at the heaviest occasion), by intervention group and time point.
Table S2. Residual normality statistics (skewness, kurtosis, and Shapiro–Wilk p‐values) for the same count outcomes.
Figure S1. Binge drinking episodes by intervention group.
Figure S2. Binge drinking episodes across time points.
Figure S3. Drinks on the heaviest occasion by intervention group.
Figure S4. Drinks on the heaviest occasion across time points.
Figure S5. Kernel density plot of residuals (with normal overlay).
Figure S6. Normal Q–Q plot.
Figure S7. Box plot of residuals.
Figure S8. Kernel density plot of residuals (with normal overlay).
Figure S9. Normal Q–Q plot.
Figure S10. Box plot of residuals.
ACKNOWLEDGEMENTS
We thank the Association of Friends of the University of Navarra and the María Egea Chair for their financial support of this project.
Contributor Information
María Lavilla‐Gracia, Email: mlavilla@unav.es.
María Pueyo‐Garrigues, Email: mpueyo.3@unav.es.
DATA AVAILABILITY STATEMENT
Data available on request from the authors.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1. Descriptive statistics (mean, SD, median, p25, p75, min, max, N) for count outcomes (binge drinking episodes and drinks at the heaviest occasion), by intervention group and time point.
Table S2. Residual normality statistics (skewness, kurtosis, and Shapiro–Wilk p‐values) for the same count outcomes.
Figure S1. Binge drinking episodes by intervention group.
Figure S2. Binge drinking episodes across time points.
Figure S3. Drinks on the heaviest occasion by intervention group.
Figure S4. Drinks on the heaviest occasion across time points.
Figure S5. Kernel density plot of residuals (with normal overlay).
Figure S6. Normal Q–Q plot.
Figure S7. Box plot of residuals.
Figure S8. Kernel density plot of residuals (with normal overlay).
Figure S9. Normal Q–Q plot.
Figure S10. Box plot of residuals.
Data Availability Statement
Data available on request from the authors.