An Adaptive Preventive Intervention to Optimize the Transition From Universal to Indicated Resources for College Student Alcohol Use
Phase: NA
Conditions: College Student Drinking
Sponsor: University of Minnesota

 
 
 
A Sequential Multiple Assignment Randomized Trial (SMART) Protocol for Empirically 
Developing an Adaptive Preventive Intervention for College Student Drinking Reduction  
(inclusive of study protocol and analysis plan) 
 
 
 
ClinicalTrials.gov ID: NCT03930524 
 
 
September 2020 

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A Sequential Multiple Assignment Randomized Trial (SMART) Protocol for Empirically 
Developing an Adaptive Preventive Intervention for College Student Drinking Reduction 
Megan E. Patricka*, Jeffrey A. Boatmanb, Nicole Morrella, Anna C. Wagnera, Grace R. Lydenb, Inbal 
Nahum-Shanic, Cheryl A. Kingd, Erin E. Bonard,e, Christine M. Leef, Mary E. Larimerf, David M. Vockb, 
& Daniel Almirallc 
 
aInstitute for Translational Research in Children’s Mental Health, University of Minnesota, 1100 
Washington Avenue South, Suite 101, Minneapolis, MN 55415, USA 
bDivision of Biostatistics, University of Minnesota, 420 Delaware Street Southeast, MMC 303 
Mayo, Minneapolis, MN 55455, USA 
cInstitute for Social Research, University of Michigan, P.O. Box 1248, 426 Thompson Street, 
Ann Arbor, MI 48106, USA 
dDepartment of Psychiatry, University of Michigan, 4250 Plymouth Road, Room 2129, Ann 
Arbor, MI 48109, USA 
eInjury Prevention Center, University of Michigan, 2800 Plymouth Road, NCRC Building 10, 
Ann Arbor, MI 48109, USA 
fDepartment of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk 
Behaviors, University of Washington, 1100 Northeast 45th Street, Suite 300, Seattle, WA 98105, 
USA 
*Contemp Clin Trials. 2020 Sep;96:106089. doi: 10.1016/j.cct.2020.106089. Epub 2020 Jul 25. 
PMID: 32717350; PMCID: PMC7494596. 
 

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Abstract 
College student alcohol use and associated negative consequences are clear public health problems with 
consequences including damage to self, others, and institutions. This paper describes the protocol of a 
research study designed to answer a number of important questions in the development of an adaptive 
preventive intervention (API) to reduce high-risk drinking among first-year college students. The API is 
designed to educate students and to motivate heavy-drinking college students to engage in existing 
resources to support reducing high-risk alcohol use, by leveraging technology-based intervention 
modalities. The primary outcome is a reduction in binge drinking, with secondary outcomes of reducing 
negative alcohol-related consequences and increasing health services utilization. Adaptive preventive 
interventions have the potential to reduce the acute and long-term negative health consequences of young 
adult alcohol use.  
Keywords: alcohol; drinking; online; adaptive; web; college 
 
 

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Introduction  
College student alcohol use, with consequences including damage to self, others, and institutions 
[1,2], is a public health problem [3,4]. About 20% of college students have an alcohol use disorder [5]. 
First-year college students are at particularly high risk [6], and prevention is needed. Preventive 
interventions are divided into subcategories [7]. Universal interventions are intended for an entire 
population, and indicated interventions target individuals who are exhibiting signs of problem behavior 
[7]. Effective universal and indicated interventions for alcohol use are available [8], with brief 
motivational interventions showing the largest effects [8–11] reducing alcohol use and consequences 
among college students [12]. However, empirically-validated interventions are underutilized, partly 
because of college students’ perceptions that alcohol use is not problematic [13,14]. Further, young adults 
are often not offered counseling [15], and are less likely to be advised to reduce their alcohol use [16]. 
Internet-based versions of brief interventions, such as Web-BASICS, are resource-efficient and have 
effects that may be as good as in-person interventions [6,17]. Additionally, the majority of college 
students already use the internet to find health information [18,19]. Therefore, technology-based 
approaches to increase utilization of empirically-supported interventions by identifying students in need 
of indicated intervention and offering online and mobile content that is attractive to students.  
A promising way to increase prevention and intervention engagement in a resource-efficient 
manner is to: 1) utilize a universal preventive intervention (given to everyone), 2) identify students at high 
risk who do not respond adequately to the universal intervention, and 3) encourage these students to 
access indicated intervention (and continue monitoring the students not (yet) identified as high risk). This 
approach is ‘adaptive’ [20] because information about the student in the course of the intervention is used 
to determine whether more resources should be invested to transition (i.e., bridge) them to indicated 
intervention. We refer to this as adaptive preventive intervention (API).1 API is conceptually consistent 
 
1 Adaptive interventions are also known as dynamic treatment regimes, multistage treatment strategies, treatment 
policies, among others [20,71] 

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with multi-tiered intervention frameworks [21–23] and stepped-care approaches to intervention that 
provide low-intensity (minimal support) interventions that are augmented for individuals showing signs 
of non-response [24–28]. By providing appropriate intervention only to those who need it, when they 
need it, APIs have potential to cost-efficiently improve outcomes and increase the reach and scalability of 
alcohol use interventions [29,30]. 
There are several open scientific questions related to how best to operationalize an API to prevent 
or reduce drinking among first-year college students. A first key question is timing. Brief interventions 
are often offered after students arrive on campus (e.g., [31,32]), but an alternative is to offer them before 
classes begin to inoculate students with accurate information before they are bombarded by campus 
influences. Students who receive normative information have not escalated into college drinking yet, so 
there may be a window of opportunity to change their perceptions. A second key question is how best to 
transition such students (i.e., heavy drinkers) to evidence-based indicated interventions, which are often 
available on college campuses [8]. To address these questions, a sequential multiple assignment 
randomized trial (SMART) design is employed whereby participants are randomized at two stages: first to 
one of two points in time at which the universal intervention is offered; and, second, among students 
identified to be at high risk following universal intervention, to one of two intervention options for linking 
them to an indicated intervention [33,34]. The purpose of this paper is to describe the protocol for a 
SMART study involving  sequential randomization yielding four APIs that are embedded in the 
experimental design.  
 
Methods 
Design Overview 
This study (called M-bridge for participants) is the first to test and optimize different components 
of an API that transitions from universal to indicated resources to reduce heavy drinking and related 
consequences among college students. Two low-cost, low-burden technology-based universal 
interventions are personalized normative feedback (PNF) and self-monitoring (SM) of alcohol use [8]. 

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Both are offered to all students in a combined universal preventive intervention (PNF+SM) but we will 
compare the timing of its delivery: before or after the start of the Fall college semester. Students who 
continue to drink heavily during the first semester of college despite PNF+SM are in need of indicated 
intervention. This study will compare two strategies for linking such students to indicated interventions: 
(1) resource emails to provide information about available online and in-person resources, maintaining 
student confidentiality and not requiring human interaction (i.e., a trained clinician), or (2) a more 
interactive, but costlier, approach involving an online dialogue with a health coach to motivate the student 
to utilize available resources. 
M-bridge employs a SMART experimental design during the first year of college, with 
interventions taking place before and/or during the first semester and final follow-up the following fall 
(semester three). At baseline, 1/3 of participants will be randomized to the control group. Of those 
randomized to intervention, half (1/3 of the total sample) will be randomized to receive an API pre-
college (early group) and half (1/3 of the total sample) will be randomized to receive an API during 
college (late group). The early group will begin their universal intervention (PNF+SM) prior to the start 
of the first semester, while the late group will begin during month one of the first semester. Students 
randomized to one of the API conditions who self-identify via SM as heavy drinkers (i.e., frequent binge 
drinking or high-intensity drinking) [35] are randomized a second time to receive an email that includes 
resource information (including a link to Web-BASICS) or an invitation to online health coaching. This 
randomization will occur as soon as a student flags for heavy drinking, so bridging to the indicated 
interventions will occur throughout the semester. 
The sequential randomizations among students who were not assigned to “assessment only 
control” result in 4 APIs that are embedded in the trial by design (Figure 1 & Table 1).  

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Figure 1. Study design 
 
Table 1. Four adaptive preventive interventions (API) embedded in the SMART.  
The shaded API (#1) is hypothesized to be optimal. 
API 
Timing of PNF+SM 
Heavy Drinker During  
First Semester? 
Bridge to Indicated 
Resources 
Cells  
(Figure 1) 
1 
Early group 
Yes 
Online health coach invitation 
B+C 
No 
N/A (continued SM) 
2 
Early group 
Yes 
Resource email 
A+C 
No 
N/A (continued SM) 
3 
Late group 
Yes 
Online health coach invitation 
E+F 
No 
N/A (continued SM) 
4 
Late group 
Yes 
Resource email 
D+F 
No 
N/A (continued SM) 
 
Aims 
Aim 1 (Primary): Examine the effect of APIs vs. assessment only on binge drinking (primary 
outcome) and negative drinking-related consequences and increased health services utilization (secondary 
outcomes). We hypothesized that students who are randomized to sequences of intervention consistent 

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with any one of the API strategies will have reduced binge drinking (primary outcome) and reduced 
negative drinking-related consequences and increased health services utilization (secondary outcomes) 
compared to those randomized to assessment only up to one year later.  
Aim 2 (Secondary): Identify the most effective and cost-effective API of the four embedded in the 
SMART (see Table 1). We hypothesized that the API that provides PNF+SM before college followed by 
online health coaching for heavy drinkers (and continued monitoring for those who are not heavy 
drinkers) will lead to the lowest frequency of binge drinking and to greatest cost-effectiveness. This 
analysis corresponding to Aim 2 will also provide estimates of the effects of (a) the timing for PNF+SM 
universal prevention, and (b) the strategy (online health coach invitation [vs. resource email including a 
link for Web-BASICS]) for motivating heavy drinkers to utilize additional resources.  
Aim 3 (Tertiary): Identify ways to more specifically tailor the API by examining the effects of 
moderators, specifically pre-college (a) alcohol use norms or (b) intentions for college drinking, on the 
effect of the timing of PNF+SM; and college (c) high-intensity drinking (i.e., 8/10+ drinks for 
women/men) vs. binge (4/5+ drinks for women/men) during self-monitoring screening on the effect of 
resource email vs. online health coach in the transition to indicated intervention. 
 
Participants and Setting  
Incoming full-time college students aged 18-21 during academic year 2019-2020 at the 
University of Minnesota Twin Cities will be eligible for participation in M-bridge. Invitations will be sent 
to approximately 1,500 incoming first-year students randomly selected from the Registrar’s list of 
admitted students and invited to participate. A simple random sample will be drawn, which given the 
large sample size should be representative of the student demographics. We will compare demographics 
(i.e., race/ethnicity, age, gender, and college of enrollment) of the sample to the population from the 
Registrar’s list to ensure that there are no significant differences. Transfer students are not eligible for 
inclusion. As this is a pragmatic trial, no exclusion criteria were used. The University of Minnesota Twin 
Cities is a large (~31,000 undergraduates), public, comprehensive land-grant University located in 

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Minneapolis and St. Paul, Minnesota. Approximately 90% of first-year students live on campus and the 
average ACT score is 28.   
Enrollment, Consent, Randomization, and Research Assessments (Survey) Procedures 
Students will be asked to participate in a baseline survey prior to their first semester of college 
(about 30 minutes in length) via mailed letter (with $5 pre-incentive enclosed) and email invitations. 
Based on prior experience, we estimate that 65% of contacted students will agree to participate, for a 
target N=700 students. The baseline survey will open for 3 weeks from the end of July until mid-August 
(classes begin 9/3/19). The beginning of the baseline survey will include online consent for the entire 
study. Participants will receive a $25 incentive after completing the survey. 
Participants will be randomized  using computer-generated, blocked randomization schemes for 
each of the two potential randomizations, available on a secure Web-based application. One third (1/3) of 
participants will be randomly assigned to assessment only control (no PNF+SM), and two thirds will be 
randomly assigned to APIs, which begin with PNF+SM. Of those assigned to APIs, half will be randomly 
assigned to the early group universal intervention (PNF+SM; students receive PNF 2 weeks before 
college classes start), and the other half will be assigned to late group universal intervention (PNF+SM; 
students receive PNF approximately 2 weeks after college classes start). That is, all participants will be 
randomly assigned initially to 1 of 3 groups in a 1:1:1 ratio: early group universal intervention vs. late 
group universal intervention vs. assessment only control.  
Participants in the early group universal intervention will be invited to engage in PNF in mid-
August and SM for 8 weeks from the beginning of September through the end of October; those in the 
late group universal intervention will be invited to PNF in mid-September and SM for 8 weeks from the 
beginning of October through the end of November. SM surveys will be sent to students every 2 weeks 
within the 8-week period. Email and text reminders will be sent 3 days after the survey is sent. Students 
may complete up to 4 SM surveys. Data from the SM will be collected using a secure Web-based 
application and used to determine whether the student is a “heavy drinker” (i.e., frequent binge drinking 
or high-intensity drinking, defined below). For each SM survey a participant completes, they will receive 

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a $2 incentive and two entries to a drawing that will occur at the end of the semester (a total of 4 drawings 
for $500 each).  
Students in the universal intervention groups who self-identify in SM as heavy drinkers are re-
randomized to one of two approaches to motivate them to access indicated services: ½ to online health 
coach invitation vs. ½ to resource email. The second randomization will occur at the first reported 
occasion of binge or high-intensity drinking as soon as the survey is submitted, and participants will no 
longer receive SM surveys. Participants who do not report heavy drinking (including those with missing 
data) will continue to be sent SM surveys. Emails will be sent to randomized participants inviting them to 
utilize either online health coaching or Web-BASICS, with up to three email reminders sent over the 
course of Fall semester if they have not yet engaged. Wording of the email invitation and one of the 
reminders informs students that the invitation to participate is based on their previous survey responses.  
The primary research outcome (survey-based report of binge drinking in the past month) and 
secondary research outcomes (alcohol-related consequences, health services utilization [e.g., online health 
coaching, Web-BASICS, campus clinics]) will be assessed with web-based surveys at the end of the first 
and second semesters of college and the following fall (one year later). Follow-up 1 at the end of 
Semester 1 (about 30 minutes), will be open the first 3 weeks of December (finals end 12/19/19). Follow-
up 2 will be open during the last three weeks of Semester 2 (finals end 5/13/20). Follow-ups 1 and 2 will 
be open before and during finals week. Follow-up 3 the following fall will be open during the second and 
third week of classes (during September 2020). Participants will receive a $30 incentive after completing 
Follow-up 1, and a $35 incentive after completing Follow-ups 2 and 3. 
All APIs will take place between August and December (i.e., immediately before or during first 
semester). Due to our questions about timing, and our design to move to indicated intervention 
immediately upon screening as a heavy drinking, there will be varying lengths of time in the intervention 
stages. However, the design and aims of the study are focused on the mode and timing versus the dosage 
of the interventions. While participants who flag early in the semester as a heavy drinker may have more 
time to engage with Web-BASICS or the online health coach, based on the scheduling of the SM surveys, 

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there would be over 3 weeks for a participant who flags on the last possible day of SM surveys to engage 
with the bridging strategies and indicated interventions before Follow-up 1 closes.  
 
Interventions 
Universal Interventions 
Personalized Normative Feedback (PNF). Personalized normative feedback interventions target 
individuals’ perceived descriptive norms for alcohol use (i.e., their perceptions of peer alcohol use) by 
contrasting their perceptions with their own alcohol use and the actual normative behavior for their peer 
group. Norms will focus on prevalence of drinking, consuming 4/5+ drinks, total number of drinks 
consumed each week, and maximum drinks consumed. Normative information will be from the college 
student sample of the national Monitoring the Future (MTF) study [4] and the University of Minnesota 
Twin Cities campus [36]. Students will be sent a link to a website where they see their own personal 
responses in the feedback. Feedback will detail quantity and frequency of drinking (a) reported by the 
participant him/herself, (b) according to the participant's perception of the descriptive norm for drinking 
quantity and frequency for the reference group (e.g., University of Minnesota first-year students, or 
typical college students), and (c) reflecting the actual descriptive norm for the reference group. For non-
drinkers, the PNF will include the same information, even if they indicated no drinking. Prior work 
suggests that presenting personalized normative feedback as a component in mailed BASICS feedback to 
abstainers and light drinkers does not have an iatrogenic effect [37]. In fact, abstainers who received 
mailed BASICS feedback were twice as likely to remain abstinent from alcohol one year later than those 
in the assessment-only control group.  
Participants will be sent email and text reminders 5 days after the PNF is sent if they have not yet 
clicked the link. The study team will have access to timestamps that indicate when participants accessed 
their PNF, how long they viewed each page of the PNF, and if they printed it. 
Self-Monitoring (SM). Students will be asked to self-report their alcohol use every in response to 
two main questions (frequency of 4/5+ drinking for women/men in the past 2 weeks, frequency of 8/10+ 

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drinking for women/men in the past 2 weeks), as well as consequences of their drinking. The therapeutic 
role of SM is two-fold. First, SM facilitates deliberate attention to and reflection on the person's actions, 
the conditions under which these actions occur, and their consequences [38,39]. Hence, SM of drinking 
behaviors and consequences has the potential to promote awareness of problematic drinking and need for 
health services. Second, SM affords the opportunity to obtain ongoing information concerning the target 
behavior, which can be used to inform timely intervention decisions [40]. Students will be asked to 
complete up to 4 self-monitoring surveys over the course of the semester.  
Embedded Tailoring Variable 
 
Every two weeks, as part of the SM intervention component, students are identified as “heavy 
drinkers” or not (including those who do not respond to the SM survey). They are identified as heavy 
drinkers if they respond to the SM and report (a) two or more occasions of consuming 4/5+ drinks for 
women/men in the past two weeks, or (b) one or more occasion of consuming 8/10+ drinks for 
women/men in the past two weeks. 
Bridging Strategies 
Online Health Coach Invitation. As a way to promote engagement in indicated intervention 
resources, an online health coaching approach will be offered to students. An email will invite students to 
engage with a health coach who will correspond with them via a secure, confidential online text-based 
chat platform. Coaching will utilize motivational interviewing strategies to encourage students to consider 
their values and goals, the possibility of behavior change, and available services. When students click the 
link in the invitation or reminder emails, they will be taken to a welcome page with a brief introduction 
(i.e., “You may have some thoughts, questions, or just want to talk about some of the things you’ve 
shared with us on surveys. Like other students, you may find it helpful to connect.”) A short menu of 
options will be available to start a health coach chat with their own message or by selecting a topic of 
interest (i.e., “The stress in your life”, “Alcohol or drug use”, “Other questions or concerns”), and there 
will be a link to a list of other resources they can access. Students and coaches will post messages online 
and notifications of new messages will be sent to the intended recipient’s email with a log-in prompt. If 

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students visit the health coach home page but do not start a chat, a health coach will reach out to them via 
email and invite them to participate. The goal of the dialogue is to motivate the student to access alcohol 
use interventions (e.g., Web-BASICS or an in-person health promotion consultation on campus). This 
approach is an adaptation of an effective intervention for offering students mental health services [41]. 
We will track engagement with the online health coach, as well as with subsequent interventions. 
Resource Email. In this approach, an email will be used to bridge students from universal to 
indicated interventions. The email will invite students to access resources to reflect on their wellness and 
alcohol use. The resources included in the email will be the same online and in-person options offered by 
the online health coach, including a link to Web-BASICS and contact information for in-person health 
promotion consultation services on campus.  
Indicated Interventions 
Web-BASICS. Web-BASICS is delivered online and incorporates to individual participant data 
with the same motivational information and feedback as used in the in-person BASICS intervention (see 
[42]). Web-BASICS contains text and graphical feedback regarding students' reported drinking quantity, 
frequency, peak alcohol consumption, and blood alcohol content (BAC), perceived norms of others’ 
drinking, risks for alcohol problems based on participant family history and consumption patterns, 
protective behaviors the participant already uses and others they might consider, and a tips page with a 
BAC chart, information on reduced-risk drinking, and where to get more information. Participants can 
print and/or view their individualized content online.  
The version of Web-BASICS utilized in the present study is very similar to the BASICS feedback 
used in traditional in-person BASICS sessions, as well as Web-BASICS used previously (e.g., [43]). The 
approach taken is very similar to previous studies, in that students filled out a survey and then 
immediately following completion of the survey the students were presented with the Web-BASICS 
personalized feedback. The normative feedback was updated based on most current response to the 
feedback and may differ from the first time they saw the PNF feedback. Prior work with Web-BASICS 
indicates college students who received Web-BASICS reported significantly lower number of drinks on 

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their peak occasion in the past month and fewer total drinking days in the past month relative to an 
assessment only control group; however, no differences were found for total drinks per week [43]. 
Health Promotion Consultation. For students who prefer an in-person approach, a health 
promotion consultant is available to all students via a campus clinic. Health promotion consultation 
includes a confidential exploration of student drug and alcohol use. Health promotion consultants help 
students assess how their alcohol and other substance use affects their life and learn how to make 
healthier decisions. 
 
Study Measures 
Outcome variables 
All study outcome variables are measured before college, at the end of the first and second semesters, and 
at the beginning of the third semester. 
 
Binge drinking (primary). The primary outcome is the frequency of consuming 4/5+ drinks for 
women/men within a two-hour period in the past 30 days [44]. 
 
Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ; secondary). Alcohol-related 
consequences experienced by respondents will be measured by 24 items derived from the 48-item Young 
Adult Alcohol Consequences Questionnaire. The B-YAACQ [45] has items that tap the full range of the 
alcohol problems continuum from signs of excessive drinking to symptoms consistent with alcohol abuse 
and alcohol dependence. 
 
Utilization of Health Services Survey (secondary). We will track engagement with Web-BASICS 
and self-reported use of health promotion services (e.g., healthcare clinics, therapy, support groups, self-
help resources, etc.) by respondents in the last 3 months, including when they started, if they are currently 
utilizing the service/resource, and how helpful it has been. We will also collect data regarding alcohol 
violations and any university-mandated alcohol interventions. 
Moderator variables 
In order to optimize the API, the following hypothesized moderators will be examined.  

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Pre-college alcohol use norms. Perceptions of other college students’ drinking (i.e., percentage 
who used alcohol in last 30 days, number of drinks consumed in typical week in last 30 days, largest 
number of drinks consumed in last two weeks, and percentage who binge drank in last two weeks) will be 
measured at baseline (before college). 
 
Pre-college intentions for college drinking. Intentions to drink (i.e., frequency of drinking, 
number of drinks consumed during typical occasion, frequency of binge drinking, and frequency of 
feeling drunk) over the next 6 months in college will also be measured at baseline. 
 
Binge and high-intensity drinking in college. The SM surveys will include measures of college 
student binge drinking (consuming 4/5+ drinks for women/men) and high-intensity drinking (consuming 
8/10+ drinks for women/men) in the last 2 weeks. 
Statistical Analysis 
All statistical analyses will follow the intention-to-treat principle, with all participants included in 
the analysis according to their randomized intervention regardless of interventions actually received. Pre-
randomization variables will be compared to check for balance between groups at each randomization. 
Primary aim analysis. We will conduct the analysis for the primary aims after data are collected 
for Follow-up 2. The analyses of the primary outcome (number out of the last 30 days with binge 
drinking) will compare the main effect of participants randomized to APIs beginning with PNF+SM vs. 
control (no API) using all of the study data: 2/3 of the sample randomly assigned to receive an API (cells 
A-F in Figure 1) versus 1/3 of the sample randomly assigned to control. The primary contrast in this 
analysis is the marginal effect of API vs. control on change in the log-average number of heavy-drinking 
days from baseline to Follow-up 1 (end of the semester one). A repeated-measures, log-link model will be 
used to analyze the longitudinal outcome. Specifically, a marginal mean model (on the log-scale) with 
terms for the intercept, dummy indicators for time (e.g., baseline, Follow-up 1, and Follow-up 2), and all 
group (API vs. control)-by-time interactions. Model coefficients will be estimated using generalized 
estimating equations [46] with an exchangeable working correlation structure between time points. 
Robust (sandwich) variance estimators will be used for hypothesis testing and confidence intervals. The 

15 
 
study’s primary hypothesis is that API results in a greater reduction in the log-mean number of binge 
drinking days per month versus control (no API) from baseline to Follow-up 1. This corresponds to 
testing the null hypothesis that the coefficient of the group-by-Follow-up 1r interaction term is zero. 
Statistical significance will be determined using a two-sided test with an α level of 0.05.  
Similar models will be fit for secondary outcomes with appropriate changes to the link function: 
log link for maximum number of drinks in the last 30 days, identity link for alcohol-related consequences 
(B-YAACQ questionnaire), and logit link for utilization of health services (yes/no). For secondary 
outcomes we will use α = 0.05 for each outcome without correcting for multiple testing. 
 
The primary analysis uses only data through Follow-up 2, but additional longitudinal through 
Follow-up 3 data are collected as part of the study. As secondary analyses, we will use the data to 
examine the temporal trend and sustainability of treatment effects (API vs. no API) for primary and 
secondary outcomes throughout follow-ups. 
Aim 2 Analyses. Data analyses comparing APIs and determining the optimal components of an 
API (Aim 2) consist of two parts. These analyses use only the 2/3 of the sample randomized to receive an 
API (cells A-F in Figure 1).   In the first part, we will conduct further analyses to identify which is the 
best, in terms of the number of heavy drinking days (primary outcome), of the four APIs embedded in the 
SMART design (Table 1). We will use the data analysis method of Robins and colleagues [47,48], 
extended for use with end-of-study [49] and for repeated measures [50,51]. The four APIs differ in terms 
of two factors (each with two levels): timing of the PNF+SM intervention (early group A1 = 1 vs. late 
group A1 = - 1), and type of bridge intervention provided to students identified as heavy drinkers (online 
health coach invitation A2 = 1 vs. resource emails A2 = - 1). The four APIs do not differ in terms of the 
intervention provided to students not identified as heavy drinkers. Note that each student will contribute 
to the API(s) with which their randomization assignment is consistent. All non-heavy drinkers are 
consistent with two APIs. For example, those students in cell C (Figure 1) are consistent with API 1 and 
API 2 (Table 2). The (longitudinal) data of those participants would be included twice in the analysis; 
once with A1 = 1 and A2 = 1 and the other instance with A1 = 1 and A2 = -1. All heavy drinkers in this 

16 
 
design are consistent with only one API. For example, those students in cell A (Figure 1) are consistent 
with API 1. Those students would be included once in the analysis with A1 = 1 and A2 = 1.  
A 2x2 weighted log-linear repeated-measures regression model for the number of heavy drinking 
days will be used. The model will include terms for the intercept, time period, time period -by-[A1, A2, 
A1-by-A2] interaction terms, and will use a working exchangeable within-person correlation structure. 
Subjects will be weighted by the inverse of their probability of randomization (weight of 2 for non-heavy 
drinkers and 4 for heavy drinkers) [50,51]. The analysis will estimate the change in average number of 
heavy drinking days at each time point for each of the four embedded APIs (with 90% CIs) and will 
report which API led to the greatest improvements. Using the same model we will also report: (a) the 
main effect of PNF+SM timing (early group vs. late group) on change, and (b) the main effect of online 
health coaching vs. resource emails on change among heavy drinkers (averaged over timing of PNF+SM).     
The second part of this analysis focuses on comparing the four APIs in terms of cost-
effectiveness relative to control (no API) [52]. A cost-effectiveness analysis for comparing the 
implementation of the four APIs will be conducted from a societal perspective. Intervention costs include 
study staff time for PNF development, programming, and participant invitations and reminders (via email 
and text); SM survey development, participant invitations and reminders (via email and text); online 
health coach platform programming and maintenance, participant invitation and reminders (via email), 
and coach training, supervision, and engagement with participants; Web-BASICS platform programming 
and maintenance, and participant invitation and reminders (via email); and health promotion consultations 
provided by Boynton Health, as well as any other resources consumed associated with developing and 
delivering each API. Time will be documented by study staff as hours per week dedicated to each 
activity. The primary measure of implementation cost-effectiveness will be incremental cost per reduction 
in number of heavy (i.e., 4/5+) drinking days in the past month. This will be calculated as the difference 
in weighted mean cost per student involved in the APIs vs control divided by the weighted mean 
difference in number of heavy drinking days between the APIs vs. control. The uncertainty of each 
incremental cost effectiveness ratio will be estimated with the bootstrap [53]. 

17 
 
Aim 3 analyses. The analysis for Aim 3 will develop a more optimal API by examining whether 
(a) pre-college alcohol use norms or (b) baseline intentions for college drinking moderate the effect of the 
timing of PNF+SM; and whether (c) high-intensity drinking (i.e., 8/10+ drinks) vs. binge (i.e., 4/5+ 
drinks) reported during college in the self-monitoring survey moderate the effect of online health 
coaching. This analysis will use Q-learning [54,55], an extension of standard moderated regression 
analysis to multiple stages of intervention; it uses a backward induction (dynamic programming) logic 
that incorporates effects of future intervention decisions in the evaluation of time-varying moderators. 
The goal of this analysis is to understand whether and how (a) or (b) could be used at baseline to decide 
which students ought to receive PNF+SM prior to college vs. in the first month of college; and to 
understand whether and how (c) could be used to decide which self-identified heavy drinkers ought to 
receive an online health coach invitation vs. a resource email.  
 
Sample size and power. The primary study contrast is a main effects comparison between API 
approaches versus control (No API) on change in frequency of binge drinking (primary outcome) from 
baseline to the end of semester 1. Using a 2-sided, 2-sample t-test based on a Type-I error rate of 5%, a 
ratio of 2:1 in the randomized allocation of individuals to APIs vs. control (first randomization), a total of 
570 students are needed to detect average differences > 1.0 to 1.15 days in past-month frequency of heavy 
drinking with > 80 to 90% power, respectively. Differences of 1 to 1.15 correspond to a small-to-
moderate standardized effect size of d = 0.25 to 0.29 [56] in between-groups change in frequency of 
heavy drinking based on previous data with standard deviation of 4 days [R01AA022113, PI: Bacharach]. 
Based on the same study suggesting an average of 7.1 heavy-drinking days in the past month, an average 
1-day reduction corresponds to a 14% reduction in heavy-drinking days. Reductions of less than 14% in 
the total number of heavy drinking days in the past month are not considered clinically significant 
because they would result in change of less than one occasion in the past month. Further, based on 
previous research (for a review see [57]) and our own pilot work, we hypothesize reductions in APIs 
relative to control with effect sizes > 0.25 (e.g., reported d ranged from 0.29 to 0.85 for PNF vs. 
assessment only) [57]. After accounting for an estimated study attrition rate of 15%, a total of at least N = 

18 
 
671 (570÷0.85) freshmen college students is needed. We anticipate this sample size is conservative but 
will invite N = 1,500 students, with an estimated response rate of 45%, giving us N = 675 participants.  
 
Summary 
Overview of Study 
The M-Bridge study will conduct a sequential multiple assignment randomized trial to (a) test 
whether or not APIs that transition from universal to indicated resources reduce heavy drinking and 
related consequences among college students, and (b) provide evidence to optimize the API including the 
timing of the universal indication (pre-college versus after college) and components of the bridging 
strategy. Effective universal and indicated prevention interventions for college students have been 
developed [8,9,12,58–60]. However, there is scant evidence to guide the development of an API for heavy 
drinking among first-year college students.   
Importance of APIs 
This study is the first to develop and optimize an API using universal and indicated interventions 
for college drinking. To our knowledge, there is no empirically-supported API for college student 
drinking, although face validity of combining universal and indicated interventions (as proposed in the 
first stage of the APIs embedded in this trial) is supported in previous work [61,62]. This study will 
provide a solid foundation for the development of additional approaches for bridging universal and 
indicated interventions (consistent with stepped-care and tiered prevention approaches), to ensure that 
students receive the level of intervention they need. APIs can capitalize on resources efficiently by using 
universal intervention – in this case an online PNF+SM intervention – as a first line of defense for all 
students. Then, students who are indicated will be motivated to seek additional intervention resources, 
because of their higher-risk drinking and greater likelihood to benefit. That is, APIs are designed to take 
advantage of heterogeneity in response to an intervention by identifying early those participants who do 
not benefit adequately from initial intervention options and modifying the intervention to accommodate 
their needs. By providing appropriate level of intervention to those who need it, when they need it, 

19 
 
adaptive interventions hold the promise of improving long-term outcomes for greater numbers of people, 
thereby increasing the reach and impact of alcohol use interventions [55,63]. 
APIs for heavy drinking among first-year college students have the potential to be high impact.  
About 20% of college students have an alcohol use disorder [5]. During the transition to college, students 
enter social networks that rely heavily on alcohol use [64,65], especially at the beginning of the academic 
year [66–68]. Many colleges and universities offer universal and indicated prevention interventions for 
their first-year students, but there is little data to help guide creation of an API.   
Novel Aspects of Design 
 
This trial is uniquely designed to answer the relevant questions for development of an API for 
heavy drinking. Because there is no empirically-supported API in this setting, we felt it important to 
establish whether APIs beginning with PNF+SM led to improved outcomes compared to no interventions. 
As a consequence, 1/3 of participants are randomized initially to an assessment-only control condition. 
Although control conditions are not required or even commonly used as part of SMART designs [69], 
initially randomizing some participants to one allows investigators to establish that APIs hold promise. 
Among those not in the control group, the two-stage randomization further allows us to collect data to 
optimize the API. In particular, intervention programs, and perhaps especially brief interventions, are 
most powerful when delivered to receptive participants at critical times for behavior change [70]. 
Therefore, the proposed design also seeks to understand the appropriate ‘windows of opportunity’ by first 
randomizing the start of the universal intervention which is a critical question for universities and other 
service providers attempting to address college alcohol use. Second, technology-based versions of brief 
interventions are resource-efficient and effects may be as good as in-person interventions [6,17]. 
Therefore, the second randomization among self-reported heavy drinkers examines whether technology-
based approaches can be used to increase utilization of empirically-supported interventions. 
Conclusions 
This paper describes the protocol of a research project designed to promote health among first-
year college students by testing and understanding how best to construct an adaptive sequence of 

20 
 
preventive intervention strategies. The interventions considered are designed to prevent and identify high-
risk alcohol use, as well as motivate heavy-drinking college students to access existing resources to 
support reducing high-risk alcohol use; in addition, the interventions leverage technology-based 
intervention modalities. The primary outcome is a reduction in binge drinking, with secondary outcomes 
of reducing negative alcohol-related consequences and increasing health services utilization. Adaptive 
preventive interventions have the potential to reduce both the acute negative health consequences (e.g., 
injury, alcohol poisoning) and long-term health consequences (e.g., alcohol use disorders) of young adult 
alcohol use, while seeking to leverage technology in order to use campus resources in the most efficient 
way possible.  
 
Acknowledgements 
This work was supported by the National Institute on Alcohol Abuse and Alcoholism grant 
R01AA026574 to M. Patrick. The content here is solely the responsibility of the authors and 
does not necessarily represent the official views of the sponsors.  
Competing interests 
The authors declare no conflicts of interest. 
 

21 
 
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