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. Author manuscript; available in PMC: 2022 Nov 1.
Published in final edited form as: Psychol Addict Behav. 2021 Jun 10;35(7):840–851. doi: 10.1037/adb0000745

Brief Alcohol Screening and Intervention for Community College Students (BASICCS): Feasibility and Preliminary Efficacy of Web-Conferencing BASICCS and Supporting Automated Text Messages

Christine M Lee 1, Jennifer M Cadigan 1, Jason R Kilmer 1, Jessica M Cronce 2, Brian Suffoletto 3, Theresa Walter 1, Charles Fleming 1, Melissa A Lewis 4
PMCID: PMC8942086  NIHMSID: NIHMS1701287  PMID: 34110840

Abstract

Objective:

The Brief Alcohol Screening and Intervention for College Students (BASICS; Dimeff et al., 1999) is an evidence-based approach to reduce high-risk drinking and associated harms; however, implementation may present challenges for community colleges (CCs) that have limited budgets and mostly non-residential students. We examined feasibility, acceptability, and efficacy of BASICS for CC students (BASICCS) delivered remotely via web-conferencing with supporting automated text messages.

Method:

Participants included 142 CC students who reported exceeding NIAAA’s weekly low-risk drinking recommendations and/or heavy episodic drinking (HED). Participants were randomized to BASICCS or assessment-only control (AOC) and completed 1- and 3-month follow-up assessments.

Results:

Most students liked the personalized information in the program and found the web-conferencing platform useful, however intervention completion rate was 56%. Significant differences were found between BASICCS and AOC. At 1-month, individuals in BASICCS had 33% fewer alcohol consequences than those in AOC. At 3-month follow-up, individuals in BASICCS had lower estimated peak blood alcohol concentration, 29% fewer drinks per week, 62% fewer episodes of HED, and 24% fewer consequences than those in AOC.

Conclusions:

BASICCS showed evidence of being acceptable and the technology proved feasible, although the intervention completion rate in the non-treatment-seeking volunteer sample was modest. Preliminary evidence does suggest BASICCS shows promise in reducing alcohol use and consequences. Technology-based platforms could be a viable prevention solution for CC students.

Keywords: Prevention, Intervention, Alcohol, College, Web-Conferencing

Young adulthood is associated with increased alcohol use (Schulenberg et al., 2020) and risk for alcohol-related negative consequences, including academic/occupational impairment, blackouts, injury, and death (e.g., White & Hingson, 2014). College students show elevated rates of high-risk alcohol use relative to their same-age non-college peers and may therefore be at greater risk for harms (Schulenberg et al., 2020). For example, 33% of college students versus 22% of their same-age non-college peers report consuming five or more drinks on a single occasion in the past 2 weeks (Schulenberg et al., 2020), a pattern associated with increased risk of consequences (White & Hingson, 2014). Although some will mature out of high-risk drinking and no longer experience the same degree of consequences, some will continue to experience problems into adulthood (Sher & Gotham, 1999), highlighting need for indicated prevention.

Of the nearly 17 million students projected to have enrolled at a degree-granting postsecondary institution in fall of 2018, approximately 39% are enrolled in 2-year institutions (National Center for Education Statistics, 2018a), most of which are community colleges (CCs). Despite the fact that CC students comprise a significant percentage of the total college student population in the United States, nearly all extant research on alcohol use, its associated harms, and effective prevention methods has focused on students attending 4-year institutions. Available data comparing students attending 2-year versus 4-year institutions suggest CC students may drink less overall and engage in less high-risk drinking than their counterparts at 4-year institutions (Cadigan, Dworkin et al., 2019; Cremeens-Matthews & Chaney, 2016; Velazquez et al., 2011), but nonetheless experience similar rates and types of alcohol-related negative consequences (Cremeens-Matthews & Chaney, 2016; Velazquez et al., 2011; Wall et al., 2012). Though lower than among students at 4-year institutions on average, rates of heavy episodic drinking (HED, typically having 4+ drinks for women/5+ for men on an occasion) among CC students are still high (from 24.2% in Pelletier et al., 2016, to 31.6% in Cadigan, Dworkin et al., 2019, and up to 49.7% in Cremeens-Matthews & Chaney, 2016), suggesting that heavy drinking is a significant concern for CC student health.

Brief Alcohol Screening and Intervention for College Students

Myriad interventions have been designed to reduce high-risk drinking and associated harms among college students. Many have growing or substantial evidence supporting their efficacy (“College Alcohol Intervention Matrix,” n.d.; Cronce & Larimer, 2011). One evidence-based approach that is widely used on college campuses across the United States is the Brief Alcohol Screening and Intervention for College Students program (BASICS; Dimeff, Baer, Kivlahan, & Marlatt, 1999). BASICS is the prototypical example of a brief motivational intervention (BMI), which is categorized as a highly effective strategy for reducing misuse of alcohol among college students (“College Alcohol Intervention Matrix,” n.d.) and traditionally involves an alcohol screening followed by an individual session. The session is led by a facilitator whose goal is to increase the student’s motivation and commitment to change their high-risk drinking behavior. In service of this goal, facilitators utilize a motivational interviewing (MI; Miller & Rollnick, 2012) style, characterized by (1) fostering collaboration with the client, (2) supporting the client’s autonomy, and (3) evoking motivation/goals from the client. Adherence to MI-style serves to elicit self-motivational statements (change talk). The session is organized around written/graphic personalized feedback regarding the individual’s drinking, related consequences, and alcohol outcome expectancies. Additionally, information on an individual’s typical and peak alcohol consumption and associated estimated blood alcohol concentrations (eBACs) is presented in relation to the individual’s perception of the drinking norms for a reference group (usually a “typical college student”) and the accurate norms. Correction of inaccurate drinking norms is a key component of BASICS. Reductions in drinking following a BASICS session are mediated through changes in normative perceptions (Larimer & Cronce, 2007). Many BMIs, including BASICS and those patterned after BASICS, also include materials promoting use of protective behavioral strategies (PBS; Martens et al., 2005), which are cognitive-behavioral strategies that an individual can learn to limit alcohol use and reduce negative consequences (Barnett et al., 2007; Larimer et al., 2007; Lee et al., 2014).

To date, there have been at least 75 randomized clinical trials of BMIs (“College Alcohol Intervention Matrix,” n.d.), most of which are direct applications of BASICS or close adaptations (Carey et al., 2007). The majority of these trials (83%) support the efficacy of BMIs in reducing drinking and consequences, with effects for BASICS specifically lasting up to 4 years (Baer et al., 2001). Meta-analytic studies have directly compared BMIs to delivery of the feedback components alone without the involvement of a MI-trained facilitator, with BMIs demonstrating some advantage (i.e., showing effects on more outcomes or with stronger effects; Cadigan et al., 2015; Carey et al., 2012), although long-term outcomes of BMIs are more nuanced (Carey et al., 2007; Carey et al., 2016) despite some support for long-term sleeper effects (White et al., 2007). Despite the support for BMIs, the vast majority (>90%) of college students ages 18–22 who meet criteria for an alcohol use disorder neither seek nor receive formal help related to their alcohol use (Wu et al., 2007). This underscores the importance of identifying novel forms of BASICS delivery that are acceptable to college students, effective at reducing hazardous alcohol use, and can be easily deployed across geographically diverse and remote student populations. Use of web-based conferencing to deliver intervention content combined with supporting automated text messages is one approach to addressing this gap in available services.

Community College Students

CCs face several challenges related to alcohol prevention (DeJong, 2006), including limited budgets, difficulty reaching off-campus students, and difficulty engaging part-time students. According to recent nationwide data, 63% of full-time and 72% of part-time public CC students work while going to school, including 38% of part-time students working full-time (Ma & Baum, 2016). Many (68%) enrolled in CC identify themselves as employees who decided to enroll in school versus students who were also employed (Berker et al., 2003). Moreover, compared to 4-year students, a greater proportion of CC students are married, parents, and single parents (National Center for Education Statistics, 2018b). As such, CC students spend the majority of their time away from the CC setting engaged in social roles that may limit their ability to come to campus for non-academic activities (e.g., in-person interventions, Wall et al., 2012). Thus, for the majority of CC students, restricting delivery of alcohol (and other mental health) services to campus is likely to pose a barrier and points to the need for other modes of intervention delivery, such as web-based conferencing.

BASICS for Community College Students: Web-conferencing Delivery with Supporting Automated Text Messages

We sought to address the above potential barrier of in-person intervention by delivering the evidence-based BASICS for CC students (BASICCS) remotely via web-conferencing with supporting automated text messaging. Web-conferencing has been shown to be feasible and acceptable for delivery of psychotherapy (Backhaus et al., 2012) and substance abuse treatment (King et al., 2014) with similar ratings of therapeutic alliance to in-person counseling (Jenkins-Guarnieri et al., 2015). For example, King and colleagues (2019) examined feasibility of providing BASICS via telehealth interaction compared to face-to-face interaction in a small study with college students and found similar changes in alcohol use between the two conditions.

Web-conferencing can provide convenience for off-campus and/or CC students who are juggling multiple roles. It also allows for unique aspects of in-person counseling absent in traditional computer-based interventions, including non-verbal cues and therapeutic alliance. Further, we modernized the delivery of information often provided in handouts or mailed postcards at the end of or following BASICS sessions to be sent with automated text messages. Although the content of the text messages in this study differed from other published text-message interventions, we sought to utilize a text-messaging platform to deliver automated alcohol-related tips and risk-reduction strategies after the web-conferencing session.

As a guide for adapting interventions, Rounsaville and colleagues (2001) propose stage 1a and 1b activities. Stage 1a activities include adapting intervention content and developing platforms for the intervention (for the current study, this included developing the content of the text messages and an additional goals-focused component of the feedback session as well as developing manualized procedures and technical support for web-conferencing and text messages). Stage 1b activities include determining participant acceptance of intervention (for the current study, this included content and delivery mechanism), ability to recruit target population, feasibility of intervention delivery, and preliminary efficacy). The present manuscript focuses on reporting Stage 1b results from a small pilot study to determine the feasibility (i.e., could the target population be recruited and would they complete the intervention) and acceptability (e.g., did those who received the intervention feel satisfied with it) of BASICCS delivered via web-conferencing with supporting automated text messages to explore potential effects on alcohol consumption and negative consequences to high-risk CC student drinkers. We recruited CC students who reported HED and/or exceeding the NIAAA’s weekly recommendations for alcohol use. We hypothesized that conducting BASICCS would be feasible and acceptable with CC students. That is, the study could recruit CC students engaged in high-risk drinking and those who were randomized to BASICCS would complete the intervention. We hypothesized that those who received the intervention would indicate it was acceptable including liking the content and finding the web-conferencing platform usable. We further hypothesized preliminary evidence showing students randomized to receive BASICCS would have greater reductions in alcohol use and consequences measured at 1-month and 3-months follow-up assessments compared to an assessment-only control group.

Method

Participants

Participants for the present study included 142 CC students from three public CCs in the Pacific Northwest. All CCs were in large urban centers. The mean age of those enrolled in the study was 22.75 years (SD = 3.34) and 69.7% of the sample identified as female. The racial/ethnic composition was 58.9% White, 12.8% Asian, 5.7% Black, 2.8% American Indian/Alaskan Native, 2.8% Native Hawaiian/Pacific Islander, 14.9% Multiracial, and 2.1% reporting other racial or ethnic identities. A total of 14.2% were Hispanic/Latinx. A total of 72.5% were full-time students and 27.5% were part-time students.

Procedures

Participants were recruited via posters and handouts placed around the campuses, newspapers ads, and direct email invitations. Students who visited the study website were presented with an information statement describing the screening process and asked to complete a 20-minute online survey to determine eligibility. Eligibility criteria consisted of being 18–29 years old; enrolled at one of the three colleges; HED (consuming 4+/5+ drinks for women/men on one occasion) in the past month or exceeding weekly NIAAA drinking recommendations (8+/15+ drinks per week for women/men) in the past month; and possessing a cell phone with text-messaging capability. Participants from all participating campuses who completed the screening survey were entered into a single drawing for $250, and those that met eligibility criteria (N=157) were immediately invited to complete the baseline survey (see Figure 1 for participant flow).

Figure 1.

Figure 1.

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Participant Flow.

The baseline survey assessed alcohol use and consequences, as well as other measures utilized in the personalized feedback (e.g., perceived alcohol use among other CC students, personal goals). Upon completion of the baseline survey, participants were stratified by sex and drinking level (i.e., meeting HED, or exceeding NIAAA weekly drinking limits, or meeting both HED and exceeding NIAAA weekly drinking limits) and randomized to one of two conditions: intervention (BASICCS) or assessment-only control. Participants assigned to BASICCS were immediately invited to schedule their web-conferencing feedback session. Participants who attended their BASICCS session were subsequently invited via email to complete an online post-intervention survey (100% completion). All participants allocated to and who received BASICCS received supporting automated text messages for 4 weeks following the intervention. Participants in both conditions completed 1-month (81.9% for BASICCS, 92.9% for control) and 3-months (81.9% for BASICCS, 91.4% for control) follow-up assessments following completion of the intervention by BASICCS participants. That is, invitations for 1-month and 3-months follow-up assessments completed by participants in the control condition were timed to arrive around the same dates as when the intervention participants were invited to their follow-up assessments; thus the follow-ups were not based on their baseline survey date. During the study, five participants in the BASICCS condition actively requested that they be removed from the trial, including three when contacted to schedule the intervention, one when contacted for the 1-month follow-up assessment, and one when contacted for the 3-month follow-up assessment.

Students were compensated $30 for baseline, $10 for post-intervention assessment, $35 for 1-month follow-up, and $40 for 3-month follow-up. The university institutional review board approved procedures, and a federal Certificate of Confidentiality was obtained from the National Institutes of Health. The clinicaltrials.gov registration number is NCT04052386.

Measures

Demographics.

Age and sex (coded 1= female and 0=male) were assessed.

Feasibility.

Feasibility was determined three ways: (1) the percentage of eligible high-risk drinkers who enrolled in the study (i.e., completed the baseline survey), (2) the percentage of students who were randomized to the BASICCS condition and who completed the intervention, and (3) the percentage of students in the BASICCS condition who read and responded to 75% (3 out of 4) randomly selected text messages that required a response. Additionally, descriptive information was collected to understand the participants’ context during their intervention (e.g., location, device used for intervention, and privacy).

Acceptability.

Acceptability of the BASICCS session was assessed in the post-intervention survey. Acceptability of the web-conferencing feedback session was assessed by asking participants to rate several statements on a scale of 1 (strongly disagree) to 7 (strongly agree), with a score of 4 being neutral. Scores of 5 or greater in relation to the web-conferencing feedback session were used to indicate overall satisfaction, attentiveness during the session, convenience, extent they liked the personalized information and messages in the BASICCS session, usefulness of the web-conferencing platform, whether they would recommend the program to a friend and overall web-conferencing experience.

Alcohol use.

A standard drink was defined as 12 oz. of 5% alcohol by volume (ABV) beer, 8 oz. of 7.5% ABV malt liquor, 5 oz. of 12% ABV wine, or 1.5 oz. (1 shot) of 40% ABV distilled spirits. The Daily Drinking Questionnaire (DDQ; Collins et al., 1985) was used to measure the number of standard drinks consumed on each day of a typical week within the past month. A summed score was created to indicate typical number of drinks in a week. Participants were also asked to indicate the number of hours they spent drinking on each day of a typical week. Number of heavy drinking episodes was assessed by asking participants the number of times in the past month they had 4+/5+ drinks for women/men in a row within 2 hours. Estimated peak eBAC was assessed using a standard formula that accounted for sex, weight, number of drinks consumed, and number of hours spent drinking (Friel Logan & Baer, 1995; Widmark, 1981).

Alcohol-related negative consequences.

Participants were asked to indicate yes (1) or no (0) if they had experienced 24 negative consequences the past month using the Brief Young Adult Alcohol Consequences Questionnaire (Read et al., 2006). Affirmative responses were summed, creating a count variable.

BASICCS

The intervention feedback session was adapted from previous work (Larimer et al., 2007; Lee et al., 2013; Lee et al., 2014; Logan et al., 2015; Turrisi et al., 2009) and consisted of a 1-hour conversation guided by personalized feedback and delivered using MI-style via VSee, a HIPAA compliant web-conferencing and screen-sharing software package. Participants randomized to the BASICCS condition were asked to select a session date and time from a variety of options presented online (including early morning, early evening and weekend times). If they could not find a time that fit their schedule, they were contacted by email or phone to try to find a time that worked for both facilitator and student. Participants in the BASICCS condition were instructed to install VSee on an electronic device of their choosing (laptop, desktop, tablet, or cell phone). They were also instructed that they must have a good internet connection during the session and told to utilize headphones to ensure audio clarity. At the scheduled appointment time, one of three doctoral-level facilitators initiated contact with the participant through VSee; all facilitators had previously been trained in MI and met for weekly consultation to discuss feedback sessions. Facilitators described the focus of the session, established rapport, and then reviewed the content of the participant’s personalized feedback using MI strategies.

The personalized feedback was tailored to each individual and included information on: (a) drinking quantity and frequency (Baer, 1993; Collins et al., 1985; Marlatt et al., 1995), (b) eBAC (including typical, peak, and time it would take to get back to 0.00%), (c) consequences the participant had experienced (Read et al., 2006), (d) gender-specific normative perceptions and actual data for other CC students (assessed with Drinking Norms Rating Form; Baer et al., 1991), (e) alcohol outcome expectancies (Fromme et al., 1993), (f) alcohol tolerance (risk based on typical peak eBAC), (g) family history risk for alcohol use disorder (Miller & Marlatt, 1984), and (h) the financial costs of alcohol (i.e., money spent on alcohol as well as money spent on other things while drinking [e.g., transportation, food, cover charges drinks for other people, gambling, tobacco, and other drugs] or as a result of drinking [e.g., internet purchases that would not have normally been made]). An additional section of the feedback summarized participants’ “5 most important goals,” as reported during the baseline assessment. Facilitators asked participants to rate how alcohol use and, conversely, reducing alcohol, affected attainment of each goal. Participants were asked to quantify their responses on a scale from 1 (very negatively) to 5 (very positively); these ratings were documented electronically in the feedback during the session. This section was designed to highlight and further develop discrepancies between values and goals of importance to the participant and ways in which the status quo related to alcohol use may run counter to these values/goals. The final pages of the feedback were dedicated to reviewing the protective behavioral strategies (PBS; e.g., set a determined time to stop drinking, planned ahead to limit the amount of money spent on alcohol) the participant reported already engaging in or, when they were not endorsed, could consider engaging in if relevant or applicable. When relevant or of interest to the participant, additional content was provided on standard drink composition, expectancy research using the balanced-placebo design, and alcohol’s biphasic effect (i.e., experience of stimulation followed by sedation). Facilitators would alternate between sharing (displaying) and unsharing (hiding) the feedback during the course of the discussion, to ensure the personal connection was maintained. This was especially important when participants used a mobile phone for the session, as only the facilitator or the feedback could be displayed at any given time, whereas on a laptop or tablet, both the facilitator and the feedback could be viewed simultaneously side-by-side. The BASICCS sessions lasted approximately 50–60 minutes.

Following the BASICCS session, a detailed version of the personalized feedback was emailed to each participant for future reference. Subsequently, participants also began receiving the supporting automated text messages over the next month. Text messages focused on information to mirror some of the strategies to reduce alcohol-related risk provided in the original BASICS intervention or included general reminders to consider their alcohol use in context of their lives. Participants received 24 text messages over 4 weeks (2 messages a day/3 days a week). Details about text message development can be found at Lewis et al. (2018).

The text message platform, compared to mailing postcards as done in prior work (Larimer et al., 2007), was chosen to provide risk-reduction tips based on student feedback in our pilot work. Text messages were sent in two separate time blocks (i.e., 3:00–5:00pm, 5:00–7:00pm, 7:00–9:00pm, 9:00–11:00pm, or 11:00pm-1:00am) based, in part, on participant preference on 3 days in each of 4 weeks. Days were matched to typical drinking days for the individual. Of the two messages sent on the text message delivery days, one message targeted times typically prior to drinking (i.e., the time block starting at either 3:00pm or 5:00pm), while the other focused on risk-reduction strategies during or after drinking (i.e., time blocks starting at 7:00pm, 9:00pm, or 11:00pm). The text messages focused on reducing alcohol-related risks and were presented in various ways, including testimonials or direct quotes from other CC students about strategies they found helpful to limit alcohol consumption (e.g., “I hate hangovers. Drinking water between drinks, and trying to stick to a limit really helps me avoid these (or at least make them less severe);” specific protective behavioral strategies (e.g., “Before drinking, consider picking a time to stop drinking and stick to it”); personalized messages from participants to themselves related to their personal goals or previous alcohol-related experiences (e.g., “Message from past self: [insert personal message from current self to future self”; e.g., “Stop and take a look around. Is it worth it?”); encouraging participants to consider how drinking would positively or negatively impact their goals (e.g., “You indicated reducing your drinking would positively impact your goal of [insert self-defined goal from VSee session]. Consider how drinking less tonight would help you meet your goal”).

Assessment-only Control Condition

The students randomized to the control condition were invited to complete 1- and 3- month follow-up assessments but did not receive any intervention services.

Data Analysis

Feasibility and acceptability were examined by (a) recruitment and completion rates of the intervention and exploration of the context of intervention participation and (b) indicators of how acceptable the intervention was for students (e.g., satisfaction, usefulness, convenience). Prior to analyses, alcohol outcomes were checked for outliers greater than 3 standard deviations above the mean and were subsequently re-coded to one unit above the highest non-outlier value, consistent with the recommendations of Tabachnick and Fidell (2013). For those in the BASICCS condition, baseline differences in drinking measures and demographic variables were examined both by intervention completion status.

Intent-to-treat analyses were conducted, using data on all randomly assigned cases, even those who did not complete the intervention. The 1- and 3-month outcomes were modeled separately to assess drinking behavior at each time point. The design of the study consisted of those in the intervention condition receiving text messages for a duration of 4 weeks following the feedback session. Therefore, the 1-month follow-up captured a time-frame in which those in the intervention condition were receiving weekly text messages. All models were adjusted for age, sex, and CC (using a dummy-coded indicator) and for the baseline measure of the given outcome. A continuous model was used for the variable peak eBAC in the past month. Standardized estimates for intervention effects on eBAC (d) were calculated based on estimates of covariate-adjusted condition differences at follow-up time points divided by the pooled standard deviation in eBAC at baseline. As the three count variables all had positive skew (range: 1.216 to 2.649 at 1-month follow-up and 1.266 to 1.946 at 3-month follow-up), a substantial number of zero values (range: 19.7% to 49.6% at 1-month follow-up and 24.4% to 53.7% at 3-month follow-up), negative binomial distributional models were used for these outcomes (see Atkins et al., 2013; Lee et al., 2013, Neighbors et al., 2010). Negative binomial models used a log link function where raw coefficients are exponentiated (i.e., raised to the base e) and interpreted as rate ratios (RRs). Rate ratios are similar to odds ratios as a value of one signifies no difference. RRs can be interpreted in terms of the percentage increase or decrease in rates associated with being in the BASICCS condition vs being in the control condition. Logistic regression models were also used to illustrate condition differences in any binge drinking episodes at 3-month follow up, since over 40% of the sample reported no binge drinking episodes at that time point. Analyses were conducted using MPlus version 7.0 (Muthén & Muthén, 2012) and IBM SPSS Statistics version 19 (IBM Corp, 2010). The primary models examining intervention differences at follow-up time points were analyzed using maximum likelihood estimation with robust standard errors, which allows for inclusion of cases with partially-missing data (i.e., cases that did complete follow-up assessments) under the assumption data are missing at random after taking into account model covariates (Graham, 2012; Muthén & Muthén, 2012).

Results

Feasibility

Participant flow is presented in Figure 1. A total of 750 individuals between the ages of 18–29 consented and completed the screening survey (another 293 completed the initial survey but were older than our target age range). Among the 750 meeting age criteria, 83.2% indicated attending one of the three partner CCs, 84.5% indicated having a mobile phone and willing to receive text messages from the study, and 31.6% met alcohol use criteria (i.e., engaging in HED and/or exceeding NIAAA weekly guidelines). A total of 157 participants (20.9% of the 750) met the three additional screening criteria and 142 (90.4% of those who screened in) completed the baseline measures and were randomized to condition. Thus, we were able to successfully recruit and screen young adult CC students engaged in high-risk drinking. Among those who did not screen into the study, 80 did meet drinking criteria but either did not attend one of the three CCs or did not have a mobile phone and agree to receive text messages.

Among the 72 students randomized to BASICCS, 40 (55.6%) completed the feedback session, 19 (26.4%) were scheduled but did not log into VSee at the time of their appointment, 7 (9.7%) were scheduled but later declined to participate before their feedback session, and 6 (8.3%) never scheduled a feedback session. Students received a series of reminders to schedule their session and/or confirming their session. Those who missed their original session were contacted again to try reschedule the session. Across the 40 students who completed the feedback session, 77.5% responded to three (25%) or four (52.5%) (out of four) text messages where a response was requested, with 3 (7.5%) people not responding to any message.

Descriptively, of students completing the session, most (82.5%) indicated they participated in the session from home, while 10% participated from school, 5% from work, and 2.5% (1 person) from someplace else. Further, 45% of students participated in their feedback session using a laptop, while 35% used their smartphone and 20% used a desktop computer. Most students (82.5%) reported they were alone when participating in their feedback session, while 17.5% reported another person was with them or in the same room (e.g., 2.5% (1 person) reported their family was there; 12.5% reported their partner (boyfriend/girlfriend) was there).

Acceptability

For the feedback session (all ratings out of 7), 95% of students agreed (25%) or strongly agreed (70%) they were attentive during their session and when viewing their personalized information (M=6.63, SD=0.67). The majority (95%) of students indicated slightly agreeing (5%), agreeing (37.5%), or strongly agreeing (52.5%) with the statement “I liked the personalized information I viewed and discussed during the VSee session” (M=6.37, SD=0.81). Similarly, 82.5% indicated agreeing or strongly agreeing they liked the overall message presented in the session (with 10% slightly agreeing; M=6.15, SD=0.89). Most (94.6%) students indicated that the web-conferencing program was a somewhat (21.6%) or very useful (73%) platform for CC students. Likewise, 80% of students indicated slightly agreeing (15%), agreeing (30%), or strongly agreeing (35%) they would recommend this program to a friend (M=5.73, SD=1.30). Close to 80% of students either slightly agreed (7.5%), agreed (45%), or strongly agreed (22.5%) with the statement “The time and length of the VSee session was convenient for me to participate in” (M=5.58, SD=1.24), with 97.1% of respondents agreeing or strongly agreeing with “Overall, I am satisfied with my VSee session,” with only 1 person saying they strongly disagreed with the statement (M=5.43, SD=1.07). Overall, 97.3% of students felt their web-conferencing experience was good (24.3%) to excellent (73%).

Baseline Descriptives and Differences by Intervention Completion

Table 1 provides descriptive information for each alcohol outcome by condition at each time-point. At baseline, participants reported an average estimated peak BAC of 0.12% (SD=0.09%), an average of 6.04 (SD=5.39) drinks per week in a typical week, an average of 1.77 (SD=2.09) binge episodes, and 4.65 (SD=4.09) alcohol-related negative consequences in the past month. Among students randomized to BASICCS, those who received the intervention did not significantly differ from those who did not receive the intervention with respect to sex, CC attended, race, ethnicity, age, working full-time, attending school full-time, being married or in a committed relationship, or scores on any primary alcohol-related outcomes (see supplemental tables for descriptives). There were no significant differences in baseline drinking (i.e., alcohol use variables, readiness to change drinking) between those who completed the intervention session and those who did not (ps > .05). Further, the potential impact of plausibly-related variables on feedback session participation was explored, revealing no statistically significant differences in any baseline cannabis use measure or baseline mental health (e.g., depression and anxiety) symptoms between completers and non-completers (ps > .05).

Table 1.

Unadjusted means of alcohol use and related consequences by condition at each time point

Baseline 1-Month Follow-Up 3-Month Follow-Up
Control
(N =70)		 BASICCS
(N = 72)		 Control
(N =65)		 BASICCS
(N = 59)		 Control
(N =64)		 BASICCS
(N = 59)
Estimated Peak BAC 0.12 (0.09) 0.13 (0.09) 0.11 (0.13) 0.09 (0.11) 0.10 (.11) 0.06 (.06)
Drinks per Week Total 6.21 (5.63) 5.86 (5.17) 5.12 (4.70) 5.38 (5.32) 5.37 (5.59) 4.09 (4.79)
Number of Binge Episodes 1.87 (1.95) 1.68 (2.23) 1.27 (1.84) 1.00 (1.61) 1.56 (2.00) 0.61 (1.13)
Alcohol-Related Consequences 4.54 (3.57) 4.75 (4.57) 4.25 (4.22) 3.28 (4.16) 3.80 (4.38) 2.88 (3.92)
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Note: Alcohol-related consequences assessed with BYAACQ.

Condition Differences in Outcomes

Table 2 provides estimates of intervention effects on drinking outcomes at 1- and 3-month follow-up. All analyses were intent-to-treat and included in the intervention condition those who were randomized but did not receive the intervention. At 1-month follow-up, individuals in the intervention condition reported 33% fewer alcohol-related consequences than those in the control condition. Differences on the three other drinking outcomes did not significantly differ by condition at the 1-month follow-up. At 3-month follow-up, all four drinking outcomes differed significantly by condition. Compared to individuals in the control condition, individuals in the intervention condition had lower estimated peak eBAC (d = −.53) and reported 29% fewer drinks per week, 62% fewer heavy drinking episodes, and 24% fewer alcohol-related negative consequences. Differences in these drinking outcomes were partly rooted in differences in prevalence of heavy episodic alcohol use. At 3-month follow-up, 57.8% of participants in the control condition reported any binge drinking episodes in the prior month versus 33.9% in the intervention condition. The odds ratio for this difference, adjusted for baseline binge drinking, sex, age and CC, is 2.84, 95% CI [1.28, 6.45], p = .012.

Table 2.

Model results for the effects of intervention condition at 1- and 3- month follow-up

1-Month Follow-Up 3-Month Follow-Up
 Outcome b (se) (95% CI) d b (se) (95% CI) D
Estimated Peak BAC −.03 (.02) (−0.06, 0.01) −.28 −.05** (.01) (−0.07, −0.02) −.53
b (se) RR (95% CI) b (se) RR (95% CI)
Drinks per Week Total −.03 (.15) 0.97 (0.72, 1.32) −.34* (.16) 0.71 (0.52, 0.98)
Number of Binge Episodes −.13 (.24) 0.88 (0.55, 1.42) −0.96*** (.23) 0.38 (0.24, 0.60)
Alcohol-Related Consequences −.41* (.19) 0.67 (0.46, 0.96) −.51* (.20) 0.60 (0.41, 0.89)
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Note.

*

p<.05,

**

p<.01,

**

p<.001;

b =Unstandardized coefficient, se = standard error, CI =confidence interval, RR = rate ratio, used in negative binominal models, d = standardized effects size difference in outcome associated with condition divided by the pooled standard deviation of peak BAC at baseline. Estimated Peak BAC modeled as continuous and normal distribution; all other outcomes modeled as negative binomial. Alcohol-related consequences assessed with BYAACQ. All outcomes assessed past month drinking behavior.

Because only 55,6% of the BASICCS condition received the intervention, we also ran the primary analysis models just including completers in the BASICCS group. The effect sizes were similar for each of the four primary outcomes at the 3-month follow-up (see tables in online supplement). Two statistically significant (p < .05) differences emerged at 1-month, with those who were assigned to the BASICCS condition and received the intervention reporting lower peak eBAC and fewer negative consequences than those assigned to the control group.

Discussion

The present research is the first study to evaluate a BMI for high-risk alcohol use among CC students. We found that the combination of a web-conferencing-delivered brief intervention modeled on BASICS with text messages was feasible to deploy from a technological standpoint, acceptable to the CC students with high-risk alcohol use who completed it, and resulted in reductions in alcohol consumption and negative consequences relative to controls. While high-risk drinking CC students enrolled in the study, overall completion rates of the intervention were low among the group of non-treatment seeking young adults randomized to intervention condition. Rates from our screening survey indicate that over 31% of young adult CC students surveyed met criteria for high-risk alcohol use, with nearly all endorsing HED in the past month, suggesting that continued research and development of efficacious alcohol interventions are warranted for this population. Results also indicate that HED is a more robust screening measure than exceeding weekly drinking guidelines, as only two students exceed weekly guidelines but did not meet HED criteria.

This work builds on previous research showing that in-person BASICS sessions conducted with 4-year college students is efficacious in reducing high-risk alcohol consumption. Our intervention is novel in that it targets the unique needs of CC students through the use of web-conferencing to deliver brief alcohol counseling. Consistent with prior research in psychiatry (Drago et al., 2016), our findings support the acceptability and usefulness of this remote counseling modality. Web-conferencing can provide convenience to students and flexibility to counselors, thus overcoming major barriers to efficient prevention delivery. Another innovation is the inclusion of text messages following the feedback session to reinforce general risk-reduction strategies and reminders to examine alcohol use in context of personal goals, which took the place of handouts and mailed postcards containing such intervention supporting content in prior intervention studies patterned after BASICS (Dimeff et al., 1999; Larimer et al., 2007). Prior research has shown text-messaging to be a useful modality to reach young adults (Bock et al., 2016; Cadigan, Martens et al., 2019; Suffoletto et al., 2014).

Our study aimed to reduce barriers to treatment engagement through web-conferencing that eliminated need to meet in person. Results indicated CC students were interested in completing surveys about health, as we had good response to recruitment procedures. However, we did not necessarily find that offering alcohol interventions via web-conferencing indicated greater reach when compared to in-person interventions; just over half of all CC students randomized to BASICCS scheduled and completed the session. Thus, while we attempted to reduce barriers, it may be the case that free time to complete a web-conferencing session is hard to come by for CC students juggling academic, work, and family obligations. At the time we conducted this study, invitations and offers of virtual/web-based meetings were not commonplace on campuses, and it is possible that there was more reluctance to accepting an invitation for and ultimately scheduling a web-conferencing session. With the realities of the COVID-19 pandemic forcing class instruction, check-ins with health center staff, counseling sessions, campus programming, and other support visits to virtual formats, it is possible that such offers and opportunities feel more acceptable and commonplace now. Regardless, future research should explore additional barriers facing CC students that could limit or reduce access to engaging in alcohol programming or interventions (e.g., work schedules, childcare, attitudes regarding telehealth).

It is also important to note that participants had responded to recruitment ads for a study about health behaviors and were not treatment or intervention seeking. Although only 56% of those randomized to BASICCS completed the session, we found it was enough to achieve important public health benefits, as indicated by the positive effects found on drinking outcomes in intent-to-treat analyses. Intervention completion rates found in this study are similar to or higher than those of other in-person interventions with non-treatment seeking college students (e.g., Turrisi et al., 2009, Wilke et al., 2014) but less than others (e.g., Martens et al., 2013; Butler & Correia, 2009). Future research needs to continue to explore how to reach high-risk students and engage them in participating in alcohol programming, especially in settings such as CCs. It may be that this intervention would be utilized less by voluntary non-treatment seeking students and more by students seeking help at college counseling or health centers; however, utilization rates of psychotherapy-related services are low among CC students (6.5%), relative to the percentage who perceive a need for help with mental health concerns (32.9%; Fortney et al., 2016), consistent with findings related to seeking services for alcohol use in 4-year college settings (Wu et al., 2007). Additionally, the 2019 survey of the Association for University and College Counseling Center Directors (AUCCCD) suggested that CC counseling centers reported serving significantly lower percentages of the student body than their 4-year private and public campus peers (LeViness et al., 2019), so efforts to expand the reach of CC counseling centers through efforts like virtual BASICCS sessions could ultimately result in more students being reached and served.

Increasingly, campuses provide strategic outreach following non-conduct-related high-risk incidents (e.g., a report referencing an intoxicated student, including hospital transports for alcohol poisoning), and this intervention could be offered to students as an option. Other forms of identification and outreach may also be necessary, such as routine alcohol screening in campus health centers and/or screening as part of fulfilling federally-mandated alcohol prevention activities with connection to services. Rather than mandating students to receive alcohol interventions, CCs could consider cost-effective ways to reward students for accessing care (e.g., reduction in fees for students who provide objective evidence of engaging in one of a menu of health promotion options each term, of which a voluntary alcohol use “check-up” could be one).

Although BASICCS was rated as acceptable to those who completed it, our results did not allow us to rate acceptability or identify-specific barriers for those who did not complete the feedback session. We did not find evidence of demographic variables or baseline alcohol use predicting intervention completion vs non-completion; however, it may be that some students prefer to not use a web-based technology platform to disclose or discuss sensitive information. It might also suggest, consistent with prior research (Wu et al., 2007), disinterest among at-risk student drinkers in receiving any counseling. Further, although the service allowed for an intervention with a focus on alcohol that otherwise would not have been provided on their campus, perhaps scheduling with someone with whom they had no prior connection and who was not directly affiliated with their campus was a barrier.

Future research will need to determine how to make web-conferencing more attractive to students who are reluctant to receive counseling, as well as to more thoroughly identify barriers to scheduling and completing an intervention via web-conferencing. Once identified, strategies for overcoming these barriers can be implemented. For example, it is possible that a missing piece of the scheduling puzzle was a “warm handoff” from someone on campus to a member of our team (e.g., Boudreaux et al., 2015; Richter et al., 2016). For example, Boudreaux and colleagues (2015) compared five different referral models for alcohol with emergency department patients that varied in referrals being made during a session or after a session, and found that 90% completed the initial consultation when the referral was made during the session vs. 10% completing the initial consultation when the referral was made after the session. Although there will still be the challenge of connecting people to care who may be experiencing risks or harms, examining ways to implement a warm handoff in the process of technology-based interventions could be a worthwhile undertaking.

Additional research is needed to determine mechanisms of change that may be specific to BASICCS (versus known mediators of other BMIs). The current study was not powered to examine mediators of intervention efficacy; thus, research should determine if mechanisms of change are present for both components; the web-conferencing feedback session and the text messages. Research has examined mechanisms of action for similar interventions for 4-year college students (Reid & Carey, 2015). Examining if similar mechanisms of action work for CC students is important to determine as interventions are developed and tested in this population.

In addition to mechanisms of change targeted in the web-conferencing feedback session, the current intervention included a text-message component focused on reducing alcohol-related risk. Recent research (Bock et al., 2016) has examined text-message interventions among CC students and shown efficacy in reducing HED and negative consequences compared to a control condition. Of interest is examining which type of risk reduction strategies, including specific protective behavioral strategies, may increase from the text messages.

Limitations

It is unclear if similar results would be found among lower-risk, light-drinking students or older students (i.e., age 30 or older). Two-thirds of the sample was female, limiting generalizability. Although attrition from longitudinal follow-up was low and comparable to other similar trials of brief interventions and appropriate missing data procedures were used in the analyses, completion rates for follow-up surveys were lower in the BASICCS than control condition (though differences were not statistically significant, p<.05). Further, the heterogeneity of the sample in regards to demographics and social roles (e.g., age, employment status, academic goals) may be important factors influencing interest in and response to the intervention. The present pilot study was not designed nor powered to test for moderation of treatment effects by these important characteristics, nor to examine differences between students who received BASICCS via laptop versus smart phone.

The study utilized an assessment-only control group and not an attention-only control group. Further, the design of the study does not allow us to draw conclusions regarding the web-conferencing innovation independent of the text messages, nor does it address the necessity of the text messages. It will be important for future work to determine whether intervention effects are increased by receiving any type of follow-up text messages or by receiving text messages with similar tips following a BASICCS session that helps to extend or reinforce intervention messages into times when drinking is more likely to occur in the real world, thus reinforcing sustained behavioral engagement. It is possible that the web-conferencing intervention may be less impactful than face-to-face BASICCS but reduced efficacy is augmented by the text component. Larger trials should compare in-person delivery of the intervention to web-conferencing and using text messages to no text messages. Additionally, research should examine the importance of the content of text messages or repeated reminders sent to students after web-conferencing to see if variation in content (e.g., an emphasis on Positive Behavioral Strategies versus more general reminders of individuals’ goals for reducing alcohol use) is related to outcomes.

Although the intervention was provided by three highly experienced clinicians in BASICS, we did not assess clinician fidelity. Future research should examine variability in facilitator effectiveness for brief motivational interventions and will need to explore BASICCS in a more widely implemented study. Finally, students’ actual drinking days may not have directly mapped onto the days of the week that text messages were received. Research is needed to examine the relative effect of timing messages to precisely coincide with actual or intended drinking versus sending messages on days typically associated with drinking.

Conclusion

Findings from the present study suggest that a significant number of CC students are at risk for HED. Moreover, this study tested novel technology-based intervention strategies (a web-conferencing feedback session and text messages) that overcome barriers many CCs face, such as funding for on-campus alcohol prevention. This line of research should continue. There is a need to identify barriers to completion of a web-conferencing feedback intervention among students engaged in high-risk drinking and examine intervention efficacy in a fully powered randomized controlled trial, with the ultimate goal of providing CCs with a viable prevention solution for their students while meeting their campus budgetary needs.

Finally, the challenges posed by COVID-19 in 2020 have led to counseling visits and other health consultations on college campuses and in the greater community to move to virtual delivery. While never the intention of the original research when it was conducted, the significance and relevance of the current study (i.e., a virtually delivered BMI that had an outcome on college student health) provides hope that current virtual counseling efforts are being impactful. As telehealth options become more widely available, research can explore ways to maximize the impact of brief interventions delivered outside of an in-person context.

Supplementary Material

Tables

Acknowledgments

Author Note

Data collection and manuscript preparation were supported by a grant from the National Institute on Alcohol Abuse and Alcoholism (R34AA023047, PI: Christine M. Lee). Manuscript preparation was also supported by NIAAA Grant F32AA025263. The content of this manuscript is solely the responsibility of the author(s) and does not necessarily represent the official views of the National Institute on Alcohol Abuse and Alcoholism or the National Institutes of Health.

Brian Suffoletto is now at the Department of Emergency Medicine, Stanford University.

Appendix A

Data Transparency – Narrative Description

Selected data from the screening and/or baseline measures have been previously published in a cross-sectional paper, however these papers are markedly different from the present manuscript. Findings from the data collection have been reported in separate manuscripts. MS1 (published) focuses on mental health symptoms and service utilization; while MS2 (published) examines open-ended health symptoms. Alcohol use is not a main outcome in either study. Since these studies only utilized screening or baseline data, randomization to intervention is not relevant or used as a predictor or covariate.

Footnotes

Public Health Statement: CC students are at risk for heavy drinking and BASICCS via web-conferencing is acceptable and associated with short-term reductions in use and consequences.

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