Using daily drinking data to characterize the effects of a brief alcohol intervention in
an emergency room
Chad J. Gwaltneya,⁎, Molly Magilla, Nancy P. Barnetta, Timothy R. Apodacaa,b,
Suzanne M. Colbya, Peter M. Montia,c
aBrown University, Center for Alcohol and Addiction Studies, Box G-S121-5, Providence, RI 02912, USA
bChildren's Mercy Hospital, University of Missouri-Kansas City School of Medicine, 2401 Gillham Road, Kansas City, MO 64108, USA
cProvidence VA Medical Center, 830 Chalkstone Ave., Providence, RI 02908-4799, USA
a b s t r a c ta r t i c l ei n f o
Screening and brief intervention
Clinical trials often aggregate daily alcohol consumption data across long-term follow-up intervals (e.g., 6 or
12 months). Although important in understanding general treatment outcomes, these analyses tell us little
about when treatment effects emerge or decline. We previously demonstrated that motivational interviewing
(MI) reduced heavy drinking (vs. personalized feedback only; FO) among young adult drinkers (N=198; ages
18–24) recruited in a hospital emergency room (ER) using aggregated drinking data from a 6-month follow
up. In the current study, weused daily alcohol consumption data from a calendar-assisted interview (Timeline
Followback) to examine the timing and course of these treatment effects. Participants in both conditions
received brief telephone booster sessions at 1 and 3 months. There were no treatment effects in the time
between the initial intervention session and the 3-month booster session. Significant effects emerged after
the 3-month booster and were driven by an increase in heavy drinking within the FO group. This suggests that
the effects of brief interventions may not emerge immediately following an initial session. Aggregated data
would be unable to detect this time trend. This research underscores the potential value added by examining
the day-to-day timing of effects following treatments for alcohol use.
© 2010 Elsevier Ltd. All rights reserved.
Alcohol and drug use data are frequently collected in clinical trials
via calendar-based methods, in which participants are asked to report
their consumption each day over a specified interval (e.g., Timeline
Followback[TLFB], Sobell& Sobell,1995; Form-90, Miller,1996). Daily
quantity and frequency data typically are aggregated over a period of
time (e.g., over the last 30 days at the 6-month follow up) to calculate
an efficacy endpoint that can be used to compare treatment groups.
Although this strategy provides important information about the
relative efficacy of the treatments, it provides little detail about the
dynamics of treatment effects over time. For example, if treatments
increase motivation to stop or reduce alcohol consumption, treatment
effects may emerge immediately following the intervention. Alterna-
tively, ifinterventions initiate a change processthatunfoldsover time,
effects may gradually appear. These patterns of alcohol use may be
better assessed in terms of fine-grained temporal fluctuations rather
than summary measures at 3- or 6-month intervals (Neal et al., 2006;
Rice, 2007; Wang, Winchell, McCormick, Nevius, & O'Neill, 2002). This
information may increase our understanding of intervention effects.
episodes, rather than just time to a single event or a single summary of
drinking frequency, has been discussed elsewhere (see multiple failure
time approach; Wanget al., 2002).Examininga singleoutcome, suchas
occurrence of relapse or average number of drinks per day, may mask
meaningful temporal trends, such as clustering of drinking episodes
overtime orwhentreatmenteffectsemerge.However,we are unaware
of any study that has used daily drinking data to examine temporal
trends in the efficacy of an alcohol intervention. The purpose of the
present research was to assess the utility of examining daily drinking
behavior in the context of a brief alcohol intervention trial. The goal of
this paper is not to assess the efficacy of the intervention (discussed
elsewhere, Monti et al., 2007), but rather to show how the examination
of daily data can enhance the description of treatment effects for
addictive behaviors, in general, by showing when the treatment effects
emerge in the course of a multi-session intervention. This approach
could provide a model for analyzing psychosocial intervention and
pharmacotherapy data that goes beyond examining aggregated data at
long-term follow-up points.
trial contrasting a single motivational interview (MI) session including
personalized feedback on alcohol use with a feedback only (FO)
condition including minimal clinician contact. The study was designed
to test these interventions in a sample of young adult problem drinkers
Addictive Behaviors 36 (2011) 248–250
⁎ Corresponding author. Department of Community Health, Brown University, Box
G-S121-5, Providence, RI 02912, USA. Tel.: +1 401 863 6662; fax: +1 401 863 6697.
E-mail address: email@example.com (C.J. Gwaltney).
0306-4603/$ – see front matter © 2010 Elsevier Ltd. All rights reserved.
Contents lists available at ScienceDirect
who were recruited in an emergency room (ER), and therefore,
considered at high risk for alcohol abuse and related injury. We
previously showed that the MI condition significantly reduced alcohol
use, when drinking data were aggregated across 6- and 12-month
periods (Monti et al., 2007). In this study, we examined daily heavy
drinking in the month prior to hospital entry and in the 6 months
following the initial intervention session. The main goal of the analysis
to describe time trends in the days leading up to the ER visit, in order to
better understand the drinking patterns that may lead to crisis events
requiring medical assistance.
Level 1 trauma center patients in a large northeastern hospital
(N=198; ages 18–24) were enrolled. Patients were eligible to
participate if they were treated in the ER and: (a) had a blood alcohol
concentration greater than .01%, or (b) reported drinking alcohol in
the 6 h prior to the event that precipitated their ER visit, or (c) scored
8 or higher on the Alcohol Use Disorders Identification Test (AUDIT;
Saunders, Aasland, Babor, De La Fuente, & Grant, 1993). Study staff
were always available, through a combination of on-site coverage
and on-call availability, eliminating sampling bias. Of the eligible
participants, 31.5% were enrolled. The study sample was primarily
male (67.7%), Caucasian (74%; followed by African–American, 11.5%),
and their average age was approximately 20 years old (20.6±1.9).
The average AUDIT score was 11.4 (± 6.4). There were no significant
group differences in consumption or alcohol problems at baseline.
See Monti et al. (2007) for more details.
participants completed a baseline assessment battery. Participants
were randomly assigned to a treatment condition (MI or FO) and
completed follow-up visits 6 and 12 months following treatment. The
6-month follow-up data are presented here (N=164; 83.3% retention
rate, with no difference between treatment groups).
2.3. Treatment conditions
Details of the treatment conditions are described in Monti et al.
(2007). Patients in the FO condition (n=100) received the same
baseline assessment and computer-generated personalized feedback
report as those in MI (n=98). However, counselor contact was
minimal (1–3 min vs. 30–45 min). Telephone booster sessions were
conducted one and three months after baseline. MI boosters included
assessment of drinking, a review of patient goals, and feedback
comparing current drinking behavior to baseline behavior. FO
boosters included an assessment of drinking. MI and FO groups did
not differ on any drinking variables, including frequency of heavy
drinking, at baseline (Monti et al., 2007). Booster compliance was
significantly higher in the FO (92%) than in the MI condition (82%) at a
1-month booster, χ2(1, N=198)=4.66, pb.05, and at a 3-month
booster (FO: 90%; MI: 74%), χ2(1; N=198)=9.09, pb.01).
The Timeline Followback (TLFB; Sobell & Sobell, 1995) was used to
assess the number of drinks per day and then days were categorized
as heavy drinking days or not (≥4 drinks for women, ≥5 drinks for
men). At baseline, the TLFB covered the 30 days prior to the ER visit.
At the 1-month booster, patients completed a 30-day TLFB. At the
3-month booster, patients completed a 60-day TLFB. At the 6-month
follow up, the TLFB covered the 30 days prior to the follow-up date.
2.5. Data analysis
Generalized estimating equations (GEE; Liang & Zeger, 1986) were
used to examine trends in daily heavy drinking over time in the two
treatment groups. Analyses addressed linear and quadratic trends in
two baseline time intervals: the entire 30 days and the 7 days prior
to the drinking episode. An autoregressive(1) correlation structure
type and empirical variance matrix were used. Because the outcome
(whether or not a day is a heavy drinking day) is binary, the
parameter estimate from the GEE can be interpreted as an odds ratio
(OR). The OR indicates the change in odds of heavy drinking, with
each passing day. Analysis of treatment group differences included all
days and the separate 1–90 and 150–180 intervals. Percent of heavy
drinking days at baseline was included as a covariate in the analysis,
group assignment was included as a categorical predictor variable
(1=MI, 2=FO), and the interaction between treatment group and
time was examined.
3.1. Pre-intervention interval
There were no linear (OR=1.00, 95% CI=0.99–1.01, p=.94) or
quadratic (OR=1.00, 95% CI=0.99–1.01, p=.56) time trends in the
30-day baseline period and treatment groups did not differ
(OR=0.82, 95% CI=0.57–1.17, p=.29) in this interval. However, a
significant quadratic trend emerged (OR=1.11, 95% CI=1.06–1.15,
pb.0001) in the week prior to the event leading to the ER visit. This
pattern was replicated in the second, third, and fourth weeks prior to
the ED visit. Since the vast majority of ER visits occurred on the
weekend, the weekly pattern appears to reflect a high frequency of
heavy drinking on the weekends and low frequency during the week
3.2. Post-intervention interval
When all days in the post-intervention period were included in
analysis, there was no significant effect of treatment, OR=1.26,
95% CI=0.88–1.80, p=.22. However, when the follow-up intervals
were considered separately, differences in the relationship between
weekly periodicity, in which heavy drinking peaks on the weekends. MI=Motivational
Intervention group, FO=Feedback Only group.
C.J. Gwaltney et al. / Addictive Behaviors 36 (2011) 248–250
treatment group and heavy drinking emerged. In the first 3 months Download full-text
post-intervention, there was no association between treatment and
heavy drinking, OR=1.13, 95% CI=0.73–1.73, p=.58. In the sixth
month, FO was significantly associated with an increased likelihood of
there were no treatment differences evident in the 3 months after the
initial intervention session. Group differences only emerged following
the 3-month booster session, such that the odds of heavy drinking on
a day were significantly higher in the FO group.
Analysis of changes over time in the post-intervention interval
suggested a nonsignificant trend towards increased heavy drinking,
OR=1.01, 95% CI=0.99–1.02, p=.08, and further analyses suggested
interaction OR=1.01, 95% CI=1.00–1.02, p=.03). In the MI group, no
linear change was observed, OR=1.00, 95% CI=0.99–1.01, p=.68, but
in the FO group, a positive trend emerged, OR=1.003, 95% CI=1.00–
1.01, p=.006 (Fig. 2). No treatment × time interactions were observed
when the 1–90 or 150–180 day intervals were analyzed separately.
Our previous analyses demonstrated the efficacy of MI vs. FO,
when past-month drinking data were evaluated at the 6-month
follow up (Monti et al., 2007). Day-to-day analysis of heavy drinking
data from the period between intervention and follow up revealed
a more nuanced picture of these significant treatment effects.
Treatment group differences only emerged after the 3-month booster
session and prior to the 6-month follow-up assessment. Without
analyzing the drinking data from smaller intervals of time, it would be
impossible to detect this important temporal trend.
We also examined the 30-day period prior to ER treatment. Patients
of periodic alcohol consumption may seem typical in a sample of 18
to 24 year olds (e.g., Del Boca, Darles, Greenbaum, & Goldman, 2004),
it is undetectable usingdescriptive measures that are typicallyincluded
in clinical trials, such as the AUDIT (Saunders et al., 1993) or 30-day
summary indices from the TLFB (Sobell & Sobell, 1995).
This analysis has some limitations to consider. This was a
secondary data analysis; the study was not designed to examine
daily drinking patterns over the entire course of the follow-up period.
For this reason, drinking on days 90 to 150 are missing. This ‘blind
spot’ in the data may obscure meaningful trends, including the exact
time when the beneficial effects of the MI treatment emerged. Fewer
MI patientsattendedtheboostersessionandthismay haveinfluenced
the results. For example, if patients who were drinking less in the
MI condition did not attend the booster, this could obscure group
differences. However, analyses of 6-month follow-up data suggest
that there were no differences in heavy drinking between those who
attended the 3-month follow up and those who did not. Approxi-
mately 17% of participants did not attend the 6-month follow up; it is
unknown how this may have affected the data. Finally, it is not clear
that these results generalize to other clinical populations; replication
in other samples is needed.
Analyzing endpoints on a daily basis can provide information
about how and when treatments begin to exert their effects and when
these effects deteriorate. It provides a more detailed description of
treatment effects than aggregate indices or single events. This more
detailed description may provide information that could be used to
improve interventions, such as highlighting time intervals when the
treatment is ineffective and supplemental treatment may be needed.
The current study may serve as a model of examining daily data in
other clinical trials.
Role of Funding Sources
This investigation was supported by research grant AA09892 from the National
Institute on Alcohol Abuse and Alcoholism, and by a Department of Veterans Affairs
Senior Career Research Scientist Award to Peter M. Monti. The funding agencies had no
role in the study design, collection, analysis or interpretation of the data, writing the
manuscript, or the decision to submit the paper for publication.
Authors 3, 5, and 6 were involved in the initial design and conduct of the study. All
authors were involved in the generation of the research question addressed in the
manuscript. Authors 1 and 2 wrote the initial drafts of the manuscript and all authors
contributed to and have approved the final manuscript.
Conflict of Interest
Chad Gwaltney serves as a consultant to PRO Consulting and invivodata, inc., which
provides electronic diary services for clinical research. All other authors declare that
they have no conflicts of interest.
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Fig. 2. Occurrence of heavy drinking days during the post-intervention interval.
MI=Motivational Intervention group, FO=Feedback Only group.
C.J. Gwaltney et al. / Addictive Behaviors 36 (2011) 248–250