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BRIEF REPORT
Meta-Analysis of Motivational Interviewing for Adolescent Health
Behavior: Efficacy Beyond Substance Use
Christopher C. Cushing
Oklahoma State University Chad D. Jensen
Brigham Young University
Mary B. Miller and Thad R. Leffingwell
Oklahoma State University
Objective: We sought to systematically review and meta-analyze the literature comparing motivational
interviewing (MI) with a control condition for adolescent health behavior change. In the current article, we
reviewed only studies targeting health behaviors other than substance use (e.g., sexual risk behavior, physical
activity, diet). Method: Systematic literature searches of PsycINFO, PubMed/Medline, and ERIC were
conducted through June 2013. Databases were combined, and studies were screened for inclusion or exclusion.
To be included in the current review, studies were required to (a) compare the efficacy of at least 1 session
of MI intervention with a control condition using a between-groups design and (b) examine a non-substance-
use health behavior in adolescents. Fifteen studies met criteria for inclusion and were described qualitatively
and quantitatively. Results: Using a fixed-effects model, we found that MI interventions produced a small, but
significant, aggregate effect size for short-term postintervention effects—g⫽.16; 95% confidence interval
(CI) [.05, .27]—compared with control conditions. Moreover, this effect was sustained at follow-up assess-
ments averaging 33.6 weeks postintervention, n⫽8, g⫽.18, 95% CI [.05, .32]. Conclusions: MI
interventions for adolescent health behavior appear to be effective. In addition, the magnitude of the aggregate
effect size does not appear to differ meaningfully from reports of interventions targeting only substance use
in adolescents. However, significant lack of clarity exists regarding interventionist training requirements
necessary to ensure intervention effectiveness.
Keywords: motivational interviewing, health behavior, adolescents, meta-analysis
Motivational interviewing (MI) has been consistently effec-
tive in promoting health behavior change among adults. In the
past 10 years, several meta-analytic reviews have demonstrated
that MI is superior to no treatment, waitlist, and information-
only treatment conditions and equivalent to active treatments in
changing health behaviors such as substance use, HIV-related
behaviors, safe sexual practices, exercise, diet, and weight loss
(Armstrong et al., 2011;Burke, Arkowitz, & Menchola, 2003;
Hettema, Steele, & Miller, 2005;Lundahl, Kunz, Brownell,
Tollefson, & Burke, 2010;Vasilaki, Hosier, & Cox, 2006).
Although effect sizes are consistently small, MI is generally
briefer than alternative treatments (sometimes as brief as a
single conversation), demonstrates effects that are robust across
delivery format and interventionist training up to 1 year postin-
tervention, and appears equally effective when delivered in
conjunction with routine medical care (Lundahl & Burke, 2009;
Lundahl et al., 2010;Lundahl et al., 2013;VanBuskirk &
Wetherell, 2013). In a recent review, Lundahl et al. (2013)
found that use of MI increases the chance of positive treatment
outcomes by 55% compared with treatment as usual.
While the evidence for the efficacy of MI in adults is robust,
less is known about the effectiveness of this technique for
adolescents. A previous meta-analytic review suggested that MI
may have a significant and sustained effect for adolescent
substance use behavior (Jensen et al., 2011), but little is known
about the efficacy of the technique for modifying non-
substance-use health behavior. It is important to understand the
utility of MI as a brief approach to changing adolescent health
behavior because recent literature has suggested that medical
professionals can be trained to use an MI style of communica-
tion with their patients (Barwick, Bennett, Johnson, McGowan,
& Moore, 2012;Söderlund, Madson, Rubak, & Nilsen, 2011). If
training and program development is to move forward, it is
important to characterize both the populations and the problem
behaviors for which MI is most effective.
This article was published Online First May 19, 2014.
Christopher C. Cushing, Department of Psychology, Oklahoma State
University; Chad D. Jensen, Department of Psychology, Brigham Young
University; Mary B. Miller and Thad R. Leffingwell, Department of
Psychology, Oklahoma State University.
Correspondence concerning this article should be addressed to Christo-
pher C. Cushing, Department of Psychology, Oklahoma State University,
116 North Murray, Stillwater, OK 74078. E-mail: christopher.cushing@
okstate.edu
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Consulting and Clinical Psychology © 2014 American Psychological Association
2014, Vol. 82, No. 6, 1212–1218 0022-006X/14/$12.00 http://dx.doi.org/10.1037/a0036912
1212
The current study examines the aggregate effect size of
studies that employ MI for adolescent health behaviors other
than substance use. A secondary aim is to describe the main-
tenance of treatment gains in studies reporting follow-up data.
This study is novel in its focus on adolescent health behavior
and extends the adolescent MI literature initially characterized
by Jensen et al. (2011).
Method
Literature Search
Comprehensive literature searches using psychological, medi-
cal, and educational databases, including PsycINFO, PubMed/
Medline, and ERIC were conducted to identify peer-reviewed
publications that reported results of motivational interventions for
pediatric health behavior. Searches were conducted using a Bool-
ean strategy for abbreviated and full keywords (motivational AND
[interview
ⴱ
OR enhancement] AND [child
ⴱ
OR adolescen
ⴱ
]).
Searches were undertaken in June of 2013.
Inclusion/Exclusion Criteria
To be included in the current review, studies were required to
(a) compare the efficacy of at least one session of MI interven-
tion to a control condition using a between-groups design and
(b) examine a non-substance-use health behavior in adoles-
cents. Studies were included if they were published in peer-
reviewed English-language journals and used quantitative
methods to evaluate the study hypotheses. Studies were in-
cluded if MI was an adjunct to a larger treatment package or
administered as a stand-alone intervention. To meet the inclu-
sion criteria, studies were required to intervene in some capac-
ity with the adolescent directly. That is, studies targeting par-
ents only were excluded.
The dependent variable of interest in the current study was
change in adolescent health behavior. Health behavior was defined
as an action performed by an adolescent that is known to mean-
ingfully increase or decrease health risk (e.g., medication adher-
ence, diet, physical activity) and did not involve tobacco or other
substance use. As indicated in Figure 1, studies were excluded for
the following reasons: (a) the study was not available in English
(n⫽15); (b) the abstract retrieved was for an unpublished disser-
tation (n⫽26); (c) the article was a review, book chapter, or other
nonempirical work (n⫽205); (d) the study design was not
experimental (n⫽118); (e) the intervention did not use MI (n⫽
9); (f) the study included a sample whose upper age limit was
greater than 21 or less than 12 (n⫽167); (g) the outcome was not
a health behavior (n⫽29); (h) the outcome was a substance-use
behavior (n⫽81); (i) the study sample was composed entirely of
psychiatric inpatients (n⫽3); (j) the study was a single group
Records idenfied through
database searching and reference
review
(n = 1,741)
Screening
Included Eligibility Idenficaon
Records aer duplicates removed
(n = 697)
Arcles assessed for
eligibility
(
n= 697
)
Full text arcles excluded
(n = 682)
(a) Not English = 15
(b) Dissertaon = 26
(c) Nonempirical = 205
(d) Not experimental = 118
(e) No MI = 9
(f) Age = 167
(g) Not health behavior = 29
(h) Substance use = 81
(i) Psychiatric = 3
(j) Single group = 12
(k) Group differences at
baseline = 4
(l) Insufficient stascs = 13
Studies included in
qualitave synthesis
(
n= 15
)
Studies included in
quantave synthesis
(meta-analysis)
(n = 15)
Figure 1. PRISMA (Preferred Reporting Items of Systematic Reviews and Meta-Analyses) flowchart.
MI ⫽motivational interviewing.
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1213
MOTIVATIONAL INTERVIEWING FOR HEALTH BEHAVIOR
design (n⫽12); (k) the study examined groups that were signif-
icantly different at baseline and did not report adjusted means (n⫽
4); and (l) the study reported insufficient statistics (n⫽13).
Analytic Plan
To ensure consistency across effect size estimates, we aggre-
gated multiple outcome measures derived from the same sample
such that each study (or group of studies using the same sample)
contributed only one effect size to the analysis. This approach
mitigates the distortion of standard error estimates resulting from
treating nonindependent studies as independent (Gleser & Olkin,
1994). Moreover, this approach provides a conservative test of the
efficacy of MI. By aggregating all outcomes reported by a single
study, our effect size estimates are agnostic to study hypotheses
regarding which outcomes would be most influenced by MI. This
approach penalizes studies that include a large number of out-
comes in the initial design but ultimately only focus on significant
results from a small number of outcomes.
Two independent observers extracted data, and disagreements
were resolved through jointly examining research reports until
agreement was reached. Studies were weighted by their sample
size using a weighted least squares approach. This method gives
greater weight to studies employing larger sample sizes (Hedges &
Olsen, 1985).
Statistical Approach
In order to arrive at an aggregate effect size for the reviewed
studies, Hedges gwas calculated for each individual study and
then aggregated using an inverse variance weight. Between-group
studies providing F,t, or means and standard deviation were
converted to Hedges g. These values were used to demonstrate the
strength of the relationship between the treatment type and the
occurrence of the specified outcome of interest (e.g., medication
adherence, sexual risk behavior, weight loss). Effect sizes with
confidence bands that did not include zero were considered statis-
tically significant, while those with confidence bands including
zero were considered nonsignificant.
Results
Description of Studies
Following application of the inclusion/exclusion criteria, 15
studies were available for data abstraction, representing a total of
1,610 participants. Half of the abstracted studies used an interven-
tionist with master’s-level or greater training in a mental health
profession while the other half of the sample included community
paraprofessionals, nurses, physicians, and dieticians (see Table 1).
Eight studies provided data after the posttreatment assessment.
These follow-up assessments ranged from 4 weeks to 2 years
(average 33.6 weeks). Other descriptive statistics are reported in
Table 1.
Overall effect size. The aggregate effect size for MI interven-
tions targeting adolescent health behaviors was small but signifi-
cant—g⫽.16; 95% confidence interval (CI) [.05, 27]; Table 2.
The Qstatistic indicated that there was not significant heteroge-
neity in the effect sizes (Q⫽17.29, p⫽.24). For this reason,
potential moderators such as intervention length, interventionist
training, and other study characteristics could not be examined as
moderators of study effect size.
Follow-up effect size. To determine the stability of treatment
effects over time, we aggregated reports of follow-up data. The
results of the analysis revealed that MI produces relatively stable
effects over time (n⫽8, g⫽.18; 95% CI [.05, .32]). The
follow-up analysis yielded a significant Qstatistic (Q⫽18.28,
p⫽.01). However, this appeared to be due to variability intro-
duced by one study (Wang et al., 2010) that reported a strong
negative effect size rather than from systematic variability among
the sample of studies. The Qstatistic was nonsignificant with
Wang et al. (2010) removed from the analysis. This suggests that
this study was the primary contributor to the variability in
follow-up effect sizes. However, Wang et al. (2010) was retained
in all analyses to avoid arbitrarily biasing the effect size estimates
due to post hoc deletion.
Fail-Safe N Calculation
To protect against the possibility that studies with a null or even
unfavorable effect size might be unpublished, we used Orwin’s
(1983) Fail-Safe N formula to determine the number of studies
required to overturn the current finding. Given that the current
mean effect size is g⫽.16, it was assumed that unpublished
studies might have a mean effect size of g⫽–.16, and the mean
standard error of .055 from the current sample was used as the
criterion for a trivial difference. Under these parameters, 34 studies
with null or negative findings would be required to overturn the
current results.
Discussion
Results of the current study indicate that MI interventions for
adolescent and child health behaviors produced small but signifi-
cant effect sizes across numerous health behavior outcomes. These
findings are consistent with previous meta-analyses demonstrating
the overall effectiveness of MI interventions targeting health be-
haviors among adults (e.g., Burke et al., 2003;Rubak, Sandbaek,
Lauritzen, & Christensen, 2005). Moreover, within the present
analysis, a subset of intervention studies employed additional
follow-up analyses, ranging from 1 month to 24 months posttreat-
ment. The effect size for this subset of studies was small, but
significant. This is particularly noteworthy, given that the average
number of intervention sessions for all studies assessed in the
present meta-analysis was 5.6, with three interventions producing
a positive effect requiring only two sessions. This is important
because it suggests that a relatively small number of meetings can
produce meaningful change. Moreover, brief interventions such as
MI allow for multiple intervention opportunities across repeated
encounters with the health care system, which could multiply the
effects of a small but reliable change and may result in larger
population improvements over time.
Reviews of the adult literature have reported superior effect
sizes for interventions targeting non-substance-use behaviors, a
finding that has been attributed to the addictive component of
substance use (Burke et al., 2003). However, when compared with
findings from previous meta-analyses of substance use behavior
(Jensen et al., 2011), the current results do not support the notion
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1214 CUSHING, JENSEN, MILLER, AND LEFFINGWELL
Table 1
Description of Studies Included in the Meta-Analysis
Author Outcome Follow-up length Age
(years) Total sample
used % male/
% female Ethnicity No. of
sessions Interventionist Weighted mean
effect size (g)
Ball 2011 Anthropometry NA 13–17 46 39.1/60.9 84.8% White 16 Registered dietician/nurse .05
Metabolic functioning 15.2% other
Physical activity
Barnet 2009 Repeat birth NA 12–19 235 0/100 97.0% African American 7–9 Community paraprofessionals .23
Cain 2011 Sleep 6 weeks 15.8–16.6 104 40.4/59.6 Not given (South Australia) 4 Psychologist and teacher .04
Channon 2003 HbA
1c
13.6 weeks 14–18 17 Not given Not given 9 Youth worker .14
Channon 2007 HbA
1c
24 months 14–18 60 48/52 100% White 4 Interventionist .54
Gourlan 2013 Anthropometry 6 months 11–18 54 59/41 Not given (France) 8 Physician and doctoral
student .06
Objective energy
Objective physical
activity
Self-reported energy
Kiene 2006 Risky sexual behavior NA College
age 149 29.7/71.3 81% White 2 Computer-based intervention .62
6.4% Hispanic
4.5% African American
8.1% other
Kong 2013 Anthropometry NA 13.9–16 51 41.2/58.8 68.6% Hispanic 8 Nurse practitioner .07
Diet 15.7% biracial
Metabolic functioning 9.8% Asian
Physical activity 5.9% American Indian
Sedentary activity
Neumark-Sztainer 2010 Anthropometry 9 months 14–17 356 0/100 28.4% African American 5–7 Interventionist .07
Diet 24.4% White
Physical activity 23.0% Asian
Restrictive eating 14.3% Hispanic
Sedentary activity 7.3% Other
2.5% American Indian
Olson 2008 Diet NA 11–20 284 51/49 96% White 2 Clinician .21
3.5% Hispanic
Resnicow 2005 Diet NA 12–16 123 0/100 100% African American 6 Master’s-level clinician .25
Seid 2012 Symptom days
(asthma) 3 months 12–18 26 69.2/30.8 76.9% African American 2 Master’s-level graduate
students .08
14.3% White
7.1% other
Viner 2003 HbA
1c
NA 10–17 21 56/44 Not given (United Kingdom) 6 Clinician .69
Walpole 2013 Anthropometry NA 12–15 40 42.5/57.5 73.3% White 6 Clinical psychology doctoral
student
⫺.10
8.3% African Canadian
3.3% Native Canadian
3.3% Asian
3.3% South Asian
8.3% other
Wang 2010 HbA
1c
9 months 13–17 44 50/50 68.2% White 2 Diabetes educators ⫺.64
31.8% Other
Note. Articles are identified by the last name of the first author and the year of publication.
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1215
MOTIVATIONAL INTERVIEWING FOR HEALTH BEHAVIOR
that substance use is more difficult to modify than other health
behaviors in adolescents. Indeed, a great deal of overlap was
observed between the confidence band in our previous work ag-
gregating substance use studies (95% CI [.08, .25]) and the current
examination of non-substance-use studies (95% CI [.05, .27]). It is
possible that future meta-analyses of adolescent studies will find
significantly larger effect sizes for non-substance-use interven-
tions, but at present there appears to be more similarity than
difference. One reason for this finding may be that substance use
interventions in adolescents tend to target subclinical problems
(Jensen et al., 2011). In cases where adolescents have developed
clinically significant substance use disorders, it would be reason-
able to suspect that effect sizes may be smaller.
Although some studies specifically indicated the amount of MI
training received by clinicians (e.g., 1-day workshop, self-directed
learning, supervised training), the majority of included studies did
not. Questions concerning level of MI training are salient, given
evidence that MI practice may not change with brief MI work-
shops (Miller & Mount, 2001). Future studies should include
information regarding the MI training obtained by clinicians,
which may be used to ascertain effects of MI-specific training (see
Miller & Rose, 2009, for a discussion).
Limitations
Results from this meta-analytic review must be interpreted in
the context of several limitations. First, our analysis of treat-
ment follow-up assessments is limited by our aggregation of
effect sizes across measurement periods. We did not account for
nonequivalent follow-up duration in the model. Thus, we were
unable to assess whether effects remain similar across varying
follow-up periods. Similarly, racially diverse samples were not
always used in the studies included in the current sample.
Therefore, the aggregate treatment effect size should be taken to
represent Caucasian American participants to a greater degree
than other racial or ethnic groups.
Future Directions
The limitations present in existing studies of the effectiveness of
MI for adolescent health behavior change suggest that several
areas of further investigation are warranted. First, few randomized
controlled trials examining the efficacy of MI for pediatric health
behavior changes have been conducted. Often considered the “gold
standard” in demonstrating treatment efficacy (American Psycho-
logical Association Presidential Task Force on Evidence-Based
Practice, 2006), more randomized clinical trials of MI used to
effect pediatric health behavior change would increase confidence
in the efficacy of MI when used with children and adolescents. Of
particular importance will be studies that examine the efficacy and
effectiveness of MI in underrepresented populations (e.g., based on
ethnicity, socioeconomic status), using designs that allow compar-
isons of MI against treatment as usual, rather than attention con-
trols.
Second, many existing MI studies have suffered from poor
treatment fidelity (Martins & McNeil, 2009). That is, the imple-
mentation of treatment protocols has been inconsistent in most
trials, preventing conclusions about whether MI is truly more
effective than standard of care interventions. No widely adopted
criteria exist for what does and does not qualify as an MI inter-
vention, making interpretation of mixed results from different
studies difficult (Resnicow, Davis, & Rollnick, 2006). Specific
behavioral coding systems (e.g., Motivational Interviewing Skill
Code (MISC; Moyers, Martin, Catley, Harris, & Ahluwalia, 2003)
2.1 and Motivational Interviewing Treatment Integrity (MITI;
Moyers, Martin, Manuel, Hendrickson, & Miller, 2005) Version
3.0) have been developed to assess treatment integrity for MI
interventions (see Madson & Campbell, 2006 for further review).
However, only five of the studies included in the current review
conducted assessments of fidelity using one of these measures,
and only three reported whether the interventionist met the
criteria for proficiency. Moreover, many interventionists who
deliver MI in health care settings received limited MI training.
Future research examining the efficacy of MI interventions
delivered by practitioners with less psychosocial training (e.g.,
physicians, nurses, and so on) will be valuable. Clearly, there is
a great need to improve assessment and reporting of clinician
training and intervention fidelity in studies of MI for adolescent
health behavior change.
Finally, the degree to which treatment effects of MI are attrib-
utable to specific treatment components (i.e., MI specific vs.
common factors) remains unclear. While this problem exists with
many empirically supported treatments, the supportive nature of
MI suggests that nonspecific factors may play an important role
(Miller & Rose, 2009). Direct comparisons of MI to other sup-
portive therapies for health behavior change will assist in answer-
ing this question.
Conclusions
Results of this meta-analysis suggest that MI is an effective
intervention to promote adolescent health behavior change. The
application of this method to adolescent populations is increasing,
and results of studies applying MI for a number of adolescent
health conditions are promising. MI can be implemented by a
variety of health care professionals (e.g., physicians, nurses, dieti-
cians; Martins & McNeil, 2009), a characteristic that is likely to
increase the reach of this technique in health-related domains. MI
interventions for adolescents are efficacious for a variety of health
behaviors, including substance use, diet, sexual health, weight
management, diabetes, and physical activity. Clinicians should
consider using MI as one component of their treatment when
attempting to help adolescents make health behavior changes.
Continued application of this technique to adolescent health be-
haviors is encouraged, and examination of the technique with new
adolescent populations is warranted.
Table 2
Summary of Mean Effect Sizes
Variable No. of effect
sizes Weighted mean
effect size (g)95% confidence
interval Q
Omnibus test 60 .16 [.05, .27] 17.29
Follow-up data 23 .18 [.05, .32] 18.28
ⴱⴱ
ⴱⴱ
p⫽.01.
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1216 CUSHING, JENSEN, MILLER, AND LEFFINGWELL
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Received November 22, 2013
Revision received March 5, 2014
Accepted March 19, 2014 䡲
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1218 CUSHING, JENSEN, MILLER, AND LEFFINGWELL
