A review of the literature on contingency management in the treatment of substance use disorders, 2009–2014

Abstract

This report describes a systematic literature review of voucher and related monetary-based contingency management (CM) interventions for substance use disorders (SUDs) over 5.2 years (November 2009 through December 2014). Reports were identified using the search engine PubMed, expert consultations, and published bibliographies. For inclusion, reports had to (a) involve monetary-based CM; (b) appear in a peer-reviewed journal; (c) include an experimental comparison condition; (d) describe an original study; (e) assess efficacy using inferential statistics; (f) use a research design allowing treatment effects to be attributed to CM. Sixty-nine reports met inclusion criteria and were categorized into 7 research trends: (1) extending CM to special populations, (2) parametric studies, (3) extending CM to community clinics, (4) combining CM with pharmacotherapies, (5) incorporating technology into CM, (6) investigating longer-term outcomes, (7) using CM as a research tool. The vast majority (59/69, 86%) of studies reported significant (p < 0.05) during-treatment effects. Twenty-eight (28/59, 47%) of those studies included at least one follow-up visit after CM was discontinued, with eight (8/28, 29%) reporting significant (p < 0.05) effects. Average effect size (Cohen's d) during treatment was 0.62 (95% CI: 0.54, 0.70) and post-treatment it was 0.26 (95% CI: 0.11, 0.41). Overall, the literature on voucher-based CM over the past 5 years documents sustained growth, high treatment efficacy, moderate to large effect sizes during treatment that weaken but remain evident following treatment termination, and breadth across a diverse set of SUDs, populations, and settings consistent with and extending results from prior reviews.

Full text

A review of the literature on contingency management in the
treatment of substance use disorders, 2009–2014
Danielle R. Davis, B.S.a,b,c, Allison N. Kurti, PhDa,b, Joan M. Skelly, MSd, Ryan Redner,
PhDa,e, Thomas J. White, PhDf, and Stephen T. Higgins, PhDa,b,c,*
aVermont Center of Behavior & Health, University of Vermont, United States
bDepartments of Psychiatry, University of Vermont, United States
cPsychological Science, University of Vermont, United States
dMedical Biostatistics, University of Vermont, United States
eBehavior Analysis and Therapy Program, Rehabilitation Institute, Southern Illinois University,
United States
fCentral California VA Healthcare System, United States
Abstract
This report describes a systematic literature review of voucher and related monetary-based
contingency management (CM) interventions for substance use disorders (SUDs) over 5.2 years
(November 2009 through December 2014). Reports were identified using the search engine
PubMed, expert consultations, and published bibliographies. For inclusion, reports had to (a)
involve monetary-based CM; (b) appear in a peer-reviewed journal; (c) include an experimental
comparison condition; (d) describe an original study; (e) assess efficacy using inferential statistics;
(f) use a research design allowing treatment effects to be attributed to CM. Sixty-nine reports met
inclusion criteria and were categorized into 7 research trends: (1) extending CM to special
populations, (2) parametric studies, (3) extending CM to community clinics, (4) combining CM
with pharmacotherapies, (5) incorporating technology into CM, (6) investigating longer-term
outcomes, (7) using CM as a research tool. The vast majority (59/69, 86%) of studies reported
significant (
p
< 0.05) during-treatment effects. Twenty-eight (28/59, 47%) of those studies
included at least one follow-up visit after CM was discontinued, with eight (8/28, 29%) reporting
significant (p < 0.05) effects. Average effect size (Cohen’s d) during treatment was 0.62 (95% CI:
0.54, 0.70) and post-treatment it was 0.26 (95% CI: 0.11, 0.41). Overall, the literature on voucher-
based CM over the past 5 years documents sustained growth, high treatment efficacy, moderate to
large effect sizes during treatment that weaken but remain evident following treatment termination,
and breadth across a diverse set of SUDs, populations, and settings consistent with and extending
results from prior reviews.
*Corresponding author at: Vermont Center of Behavior & Health, University of Vermont, United States.; Stephen.Higgins@uvm.edu
(S.T. Higgins).
Competing interests
The authors have no conflicts of interest to report.
Transparency document
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Keywords
Contingency management; Financial incentives; Substance use disorders; Psychosocial
interventions; Behavioral interventions; Behavioral economics; Behavior change
1. Introduction
Substance use disorders (SUDs) are highly prevalent in the U.S. as they are in other
developed countries. These disorders undermine health and longevity and are tremendously
costly economically. In the U.S. population, for example, approximately 20% of adults
report past month tobacco use, 25% report past month risky alcohol use (e.g., binge
drinking), and 10% report past month illicit drug use (SAMSHA, 2014). Excessive use of
tobacco, alcohol, and illicit drugs in the U.S. each year is estimated to result in N600
thousand premature deaths, 166 billion dollars in U.S. annual healthcare costs, and 700
billion dollars in overall annual costs related to crime, lost work productivity, and healthcare
combined (U.S. Department of Health and Human Services, 2014; CDC, 2014; NDIC,
2011). Similar patterns of use and adverse consequences are well documented
internationally (UNODC, 2015). Given these enormous adverse impacts, the development of
more effective treatments for SUDs is a critically important public health priority.
Contingency management (CM) interventions wherein financial incentives are provided
contingent on objective evidence of behavior change have shown impressive levels of
efficacy across a wide range of SUDs (Higgins et al., 2008). Early iterations of this general
treatment model were first reported in the late 1960s (Elliott and Tighe, 1968) and further
refined in the 1970s and 1980s, typically among opioid-dependent populations enrolled in
methadone-based substitution therapy (Stitzer and Higgins, 1995). Voucher-based CM
wherein individuals receive vouchers exchangeable for retail items or other financial
incentives was introduced in the early 1990s as part of efforts to develop efficacious
outpatient treatments for cocaine dependence (Higgins et al., 1991, 1993). The success of
that model in promoting sustained periods of cocaine abstinence was associated with an
expansion of CM research to other SUDs and an acceleration of research on this treatment
strategy (Fig. 1).
Along with this growth in CM research came the need for periodic literature reviews. Our
group previously published two comprehensive literature reviews on this topic (Higgins et
al., 2011; Lussier et al., 2006), with the present report representing the third in what is
intended to be a systematic series that covers the time period from the introduction of the
voucher model in 1991 to the present. The initial review was a meta-analysis comprising 40
controlled studies published in peer-reviewed journals between January 1991 and March
2004 (13.25 years). The review focused exclusively on treatment effects during the
intervention period. There was overwhelming evidence of efficacy of this treatment
approach for increasing abstinence from drug use and retention in treatment across a wide
range of different types of SUDs, with overall effect sizes in the moderate range (Lussier et
al., 2006). That review also examined potential moderators of treatment efficacy identifying
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two that significantly increased effect size (i.e., higher incentive monetary values and shorter
delays in delivering incentives).
The second review in this series was a narrative review that again examined controlled
studies published in peer-reviewed journals (Higgins et al., 2011). The time period of
interest was from April 2004 (i.e., the end date in the prior review) to October 2009 (5.5
years). Sixty-seven reports met inclusion criteria, a more than three-fold increase in
publications per year over the earlier review. The results were consistent with the earlier
review in providing overwhelming evidence of efficacy, with 59 of the 67 (88%) reports
noting significant treatment effects. Importantly, this second review also organized the
growth in the CM literature into seven trends, a practice that is continued in the current
review.
Important to mention is that there have been other reviews of the CM literature. One focused
on CM involving a wide range of different types of incentives (Prendergast et al., 2006),
another was limited to only CM studies that used a probabilistic schedule of incentive
delivery (Benishek et al., 2014), still another on CM interventions implemented in outpatient
methadone maintenance clinics (Griffith et al., 2000), and one that examined only CM
studies that included a cost-effectiveness analysis (Shearer et al., 2015). However, none of
those reviews duplicate the focus of the present systematic review on contributing to a series
exclusively focused on the use of vouchers and related financial incentives among those with
SUDs.
In the two prior reports in this series, several gaps in the CM literature were identified.
Lussier et al. (2006) called for additional studies on the influence of incentive value/
magnitude and CM duration on treatment efficacy. Higgins et al. (2011) recommended that
future studies address technical obstacles that, at the time the review was written, had
precluded the use of CM with certain SUDs (e.g., alcohol) or to individuals residing in
geographically remote areas. Both reports recommended that future research on this topic
should evaluate the maintenance of treatment gains after the incentives were discontinued.
The aims of the present review include (1) characterizing the foci and outcomes of CM
studies published in the 5.2 year period between November 2009 through 2014, (2)
characterizing research trends across this most recent period compared to the prior reviews,
and (3) evaluating whether previously identified gaps in the CM literature have been
addressed.
2. Methods
The methods employed in the current review are based on those used in Higgins et al.
(2011). More specifically, the literature search was conducted using Pubmed, the search
engine of the U.S. National Library of Medicine. Pubmed was searched using the term
“vouchers OR contingency management,” targeting articles published between November 1,
2009 through December 31, 2014. Reference sections of review papers retrieved by this
search were also searched. All articles were reviewed for inclusion by at least two of the
authors and discrepancies resolved through discussion.
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For inclusion, reports had to (a) involve a monetary-based CM intervention; (b) appear in a
peer-reviewed journal; (c) include an experimental comparison condition; (d) report during-
treatment results from an original, prospective experimental study; (e) examine treatment
effects using inferential statistics; and (f) use a research design allowing attribution of
treatment effects to CM. These criteria differ from the inclusion criteria used in the initial
review where they also required that studies focus on individuals enrolled in formal
treatment and have a sample size ≥10 individuals (Lussier et al., 2006), but match those used
in the immediately preceding review (Higgins et al., 2011).
The reports included in the current review were characterized in terms of the behavior
change targeted by the CM intervention, whether CM produced statistically significant (p <
0.05) treatment effects, whether the study examined post-incentive treatment effects, the size
(Cohen’s d) of treatment effects reported, and discernible trends in the CM literature. Effect
sizes were not quantified in the 2011 review. A study was categorized as having a significant
treatment effect if effects of the CM intervention on at least one primary outcome during
treatment or at treatment termination assessment were significant (p < 0.05). Similarly, a
study was categorized as having a significant effect at follow-up if significant treatment
effects on one or more primary outcomes remained significant following discontinuation of
incentives (p < 0.05). As such, only those studies reporting significant during-treatment
effects were evaluated for post-incentive treatment effects, with the focus on examining the
sustainability of treatment effects once incentives were discontinued. In contrast to the prior
review wherein articles were categorized into only a single primary trend, articles in the
current review were also assigned to secondary trends when appropriate (see Tables 1–4).
Identifying additional trends in the literature was not part of the initial review. Authors
worked in pairs in identifying trends with any discrepancies resolved through discussion.
Cohen’s d was used as the measure of effect size and was calculated for each study, except
for ten reports that did not have enough information available to calculate an effect size
(Killeen et al., 2012; Krishnan-Sarin et al., 2013; Kurti and Dallery, 2014; Meredith et al.,
2011; Ondersma et al., 2012; Reback et al., 2010; Tuten et al., 2012; Walker et al., 2010;
Winstanley et al., 2011; Wang et al., 2014). For continuous outcomes, study effect sizes
were computed based on the reported test statistic. If an appropriate test statistic was not
available, effect sizes were computed based on means and standard deviations (or standard
errors) presented in the text, tables or figures. For dichotomous outcomes, study effect sizes
were computed based on the odds ratio. If an odds ratio was not reported, a 2 × 2 table was
constructed from the reported percentages and the odds ratio was calculated. Odds ratios
were then converted to Cohen's d. For studies involving multiple incentive-based treatment
conditions, a single effect size was obtained by combining the incentive-based treatment
conditions and calculating the effect size for the combined treatment conditions versus
control condition or by calculating a weighted average of the individual effect sizes for each
incentive-based treatment condition relative to the control condition. Random effects meta-
analysis models were used to calculate the estimated average effect sizes and the
corresponding 95% confidence intervals across studies and within the seven trends. These
models were analyzed using Comprehensive Meta-Analysis 3.0 (Biostat Inc., Englewood,
NJ).
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3. Results
3.1. Overall search results
The search identified 801 reports for review, of which 69 (9%) met inclusion criteria. Of the
732 studies excluded, 589 (80.5%) did not involve monetary-based CM, 114 (15.6%) did not
report results from an original, experimental study, 23 (3.1%) did not use a research design
allowing treatment effects to be attributed to CM, and 6 (1%) did not include an
experimental comparison condition.
Sixty-nine articles across 5.2 years represent an annual publication rate of 13.3 reports. Most
of these studies focused exclusively on increasing abstinence from drug use (51 studies or
73.9%). Ten studies (14.4%) exclusively targeted another therapeutic goal, and 8 (11.5%)
targeted both abstinence and another therapeutic goal.
CM was highly efficacious across the three different targets, with 59 of 69 studies (86%)
reporting significant treatment effects. Among studies exclusively targeting abstinence, 43 of
51 studies (84%) reported significant treatment effects; 9 of 9 studies (100%) exclusively
targeting other therapeutic goals reported significant treatment effects, as did 7 of 9 studies
(78%) targeting both abstinence and other therapeutic goals. Average effect size among
studies reporting significant during-treatment effects was 0.66 (95% CI: 0.58, 0.75). Average
effect size across all studies examining during-treatment effects was 0.62 (95% CI: 0.54,
0.70).
Among the 59 studies reporting significant treatment effects, 28 (47%) included at least one
follow-up assessment after the incentives intervention was discontinued, with eight of these
28 studies (29%) reporting significant treatment effects at one or more follow-up visits.
Average effect size across studies reporting significant post-treatment effects was 0.43 (95%
CI: 0.24, 0.62). Average effect size across all studies examining post-treatment effects was
0.26 (95% CI: 0.11, 0.41).
3.2. Trends in the literature
The 69 reports meeting inclusion criteria were categorized into seven trends, six of which
were consistent with those in the immediately prior review (Higgins et al., 2011). Trends
represented in both reviews include (1) extending CM to special populations, (2) conducting
parametric studies, (3) extending CM to community clinics,(4) combining CM with
pharmacotherapies, (5) investigating longer-term outcomes, and (6) using CM as a research
tool. The present review also includes an additional trend of (7) integrating novel
technologies (e.g., Smartphones) into CM. In the prior review an additional trend identified
was extending CM to new SUDs, but that was not a primary aim in any of the studies in the
current review. Below we comment on the above trends starting with those involving the
largest number of reports and working to the least, while giving priority to primary trends.
3.2.1. Extending CM to special populations—Extending CM to special populations
(e.g., adolescents, pregnant women) is the trend under which the most studies were
categorized (23/69, 33%) (Table 1). Eighteen of these 23 studies (78%) targeted abstinence,
2 (9%) targeted abstinence and another outcome, and 3 (13%) targeted another outcome
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only. Nineteen of the 23 studies (83%) reported significant treatment effects. Effect sizes
could be calculated for 17 of those 19 studies, the average of which was 0.67 (95% CI: 0.53,
0.82). Effect sizes could be calculated for 20 of the total 23 studies categorized under this
trend, the average of which was 0.64 (95% CI: 0.49, 0.78). No systematic differences in
efficacy by CM target or population were noted.
Twelve studies with significant during-treatment effects also reported outcomes following
discontinuation of the incentives, with four (33%) of those studies reporting significant
treatment effects at one or more follow-up assessments. Effect sizes could be calculated for
three of those four studies, the average of which was 0.27 (95% CI: 0.10, 0.43).
Among the 15 studies categorized under this trend that included one or more follow-up
assessments, effect sizes could be calculated for 10, with an average effect size of 0.23 (95%
CI: −0.01, 0.47).
Regarding illicit drug use disorders, studies were reported on CM reducing marijuana use
(Kaminer et al., 2014; Stanger et al., 2009) among adolescents, and psychomotor stimulant
and poly-drug use among those with co-morbid mental illness (García-Fernández et al.,
2013; Kelly et al., 2014; McDonell et al., 2013; Petry et al., 2013), socioeconomic
disadvantage (Secades-Villa et al., 2013), pregnant women and mothers of young children
(Schottenfeld et al., 2011), sexual minorities (specifically men who have sex with men,
Menza et al., 2010; Reback et al., 2010), those with HIV infection (Petry et al., 2010), and
military veterans (Hagedorn et al., 2013).
Cigarette smoking is highly prevalent in the special populations listed above and nine of the
24 reports in this trend (38%) focused on smoking cessation, including studies with
socioeconomically disadvantaged pregnant (Higgins et al., 2014; Ondersma et al., 2012) and
non-pregnant adults (Kendzor et al., 2014), adolescents (Krishnan-Sarin et al., 2013),
homeless individuals (Businelle et al., 2014), and individuals with co-morbid SUDs (Alessi
and Petry, 2014; Drummond et al., 2014; Dunn et al., 2010) or mental illness (Hertzberg et
al., 2013). This application of CM is still in the initial efficacy-testing stages of development
for most of these populations except for pregnant women where research is now focused on
late-stage efficacy and cost-effectiveness testing (see Higgins and Solomon, 2016).
3.2.2. Investigating parametric questions—Investigating questions about how
altering CM parameters impacts treatment outcomes is obviously an important area of
inquiry (Table 2). In the current review, 11 out of 69 studies (16%) were categorized under
this trend. Ten of the 11 studies (91%) targeted abstinence, and one (9%) targeted both
abstinence and another therapeutic goal. Ten studies (91%) reported significant during-
treatment effects with an average effect size of 0.63 (95% CI: 0.44, 0.82). The one study that
failed to produce a significant during-treatment effect also failed to include sufficient
information to calculate an effect size. Thus, the overall effect size is the same as the one
reported above for studies producing significant effects.
Six studies with significant treatment effects also reported outcomes following
discontinuation of incentives, with one study (17%) reporting significant treatment effects at
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follow-up and an effect size of 0.43 (95% CI: −0.004, 0.86).A total of seven studies within
this trend included one or more follow-up assessments. An effect size could be calculated for
three of those seven studies, with an average of 0.33 (95% CI: −0.20, 0.86).
Parameters investigated included durations of the incentives intervention (Kirby et al., 2013;
Roll et al., 2013), effect of delay in incentive delivery (Packer et al., 2012), cash versus
voucher incentive type (Festinger et al., 2014), incentive monetary value (Packer et al., 2012;
Petry et al., 2012c; Petry et al., 2014a; Romanowich and Lamb, 2010), incentive schedule
(Ledgerwood et al., 2014; Romanowich and Lamb, 2013; Tuten et al., 2012; Lamb et al.,
2010), and efficacy of CM in combination with a novel treatment component (Kurti and
Dallery, 2014). Results from these studies indicated that increased incentive duration and
value generally improves outcomes (e.g., Kirby et al., 2013; Roll et al., 2013) and delays
between verification of abstinence and incentive delivery diminishes outcomes (Packer et al.,
2012). A study comparing cash and voucher incentives noted that both were efficacious
although the former may be more cost-effective (Festinger et al., 2012). The majority of
studies evaluating incentive magnitude reported that higher magnitude incentives were more
efficacious (Packer et al., 2012; Petry et al., 2012c, Romanowich and Lamb, 2010), although
one did not (Petry et al., 2014a). Parametric studies evaluating schedule of incentives varied
greatly. Two studies illustrated that higher values of incentives at the beginning of a schedule
do not improve outcomes (Ledgerwood et al., 2010; Romanowich and Lamb, 2013—see
also Higgins et al., 2014 categorized under special populations); another study reported no
difference between a fixed and escalating schedule of incentives, although authors attributed
lack of differences to delay in incentive delivery (Tuten et al., 2012). Another study
evaluating incentive schedule among hard-to-treat (HTT) versus easy-to-treat (ETT) smokers
using schedules with or without shaping reported that ETT smokers responded to either
schedule, whereas HTT smokers had better outcomes with shaping (Lamb et al., 2010).
3.2.3. Extending the intervention into community clinics—Extending CM into
community clinics remained a clear trend in the current review (Table 2). Twelve of the 69
studies (17%) were categorized under this trend, with six (50%) targeting abstinence, four
(33%) targeting abstinence and another outcome, and two (17%) targeting another outcome
only. Ten studies (83%) reported significant during-treatment effects. An effect size could be
calculated for nine of the studies reporting significant during-treatment effects, with an
average of 0.53 (95% CI: 0.34, 0.73). An effect size could be calculated for 10 of the 12
studies categorized under this trend, with an average of 0.50 (95% CI: 0.31, 0.70).
Four of the studies with significant treatment effects also reported outcomes following the
discontinuation of incentives with one of the four (25%) reporting significant effects at
follow-up and an effect size of 0.53 (95% CI: 0.05, 1.02). A total of six studies within this
trend included one or more follow-up assessments. An effect size could be calculated for
four of those six studies, with an average of 0.27 (95% CI: −0.09, 0.63).
Across the twelve studies in this trend, the approaches to implementing CM in community
clinics included training clinicians to deliver the treatment (Petry et al., 2012a; Petry et al.,
2012b), including CM in group therapy sessions (Branson et al., 2012; Killeen et al., 2012;
Petry et al., 2011; Walker et al., 2010), and disseminating the treatment to community clinics
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internationally (Chen et al., 2013; García-Fernández et al., 2011; Hser et al., 2011; Jiang et
al., 2012; Petitjean et al., 2014; Secades-Villa et al., 2014). Both studies that focused on
training community clinicians to deliver CM reported significant treatment effects (Petry et
al., 2012a; Petry et al., 2012b). Among the four studies where CM was added to group
counseling, three reported significant effects on attendance (Branson et al., 2012) or both
attendance and abstinence outcomes (Petry et al., 2012a; Petry et al., 2012b), whereas one
study reported no effect of CM on abstinence (Killeen et al., 2012). The lack of treatment
effects in this latter study was attributed to using very low magnitude incentives with a
difficult-to-treat population (i.e., marijuana dependent adolescents).
Another set of studies in this category investigated implementation of CM in community
clinics internationally. For example, CM was implemented in two studies conducted in
community clinics in Spain, where it was effective in reducing cocaine use (García-
Fernández et al., 2011) and cigarette smoking (Secades-Villa et al., 2014). A Swiss group
evaluated the relative efficacy of CM + Cognitive Behavior Therapy (CBT) vs. CBT alone in
treating cocaine dependence in a community clinic (Petitjean et al., 2014). CM + CBT
produced greater during-treatment but not post-treatment cocaine abstinence. Lastly, three
CM studies were conducted in methadone maintenance clinics in China to promote both
attendance and abstinence. CM significantly increased attendance and abstinence in two of
those studies (Chen et al., 2013; Hser et al., 2011) but not the third (Jiang et al., 2012).
Collectively, the above studies provide sound support for the efficacy of CM when
implemented in community clinics in the U.S. and abroad.
3.2.4. Incorporating new technologies into CM—CM interventions often entail
frequent objective monitoring of target behaviors, which can be cumbersome on clinical staff
and patients alike and also limit the reach of CM. Investigators are increasingly utilizing
remote behavior-monitoring technologies to surmount this problem (see Kurti et al., 2016,
for a review). That and other novel technological advances described below led us to include
this additional trend (Table 3). Eight of the 69 studies (11.5%) were categorized under this
trend, with seven (87.5%) targeting abstinence from cigarette smoking (three studies) and
alcohol use (four studies) and the eighth (12.5%) targeting adherence to remote monitoring
of cocaine craving. All eight studies (100%) reported significant treatment effects. An effect
size could be calculated for seven of those 8 studies, with an average of 0.70 (95% CI: 0.42,
0.98). Only one of these studies evaluated post-incentives maintenance of treatment effects,
which were not significant; the effect size was −0.05 (95% CI: −0.85, 0.76) (Dallery et al.,
2013).
The three studies targeting cigarette smoking monitored smoking status by having
participants use a web camera to submit time-stamped videos of breath carbon monoxide
(CO) testing over a study website (Dallery et al., 2013; Meredith et al., 2011; Meredith and
Dallery, 2013). Two of those studies also included group contingencies wherein teams of
participants communicated with each other over an online support forum (Meredith et al.,
2011; Meredith and Dallery, 2013).
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Two of the four studies targeting alcohol monitored use with the Secure Continuous Remote
Alcohol Monitor (SCRAM) device, a real-time, continuous, transdermal monitor (Barnett et
al., 2011; Dougherty et al., 2014). In the third study, alcohol abstinence was increased while
monitoring use through time-stamped videos of participants blowing into a breathalyzer
using a cellphone (Alessi and Petry, 2013). The fourth study targeting alcohol increased
abstinence while monitoring alcohol intake by analyzing twice-weekly urine samples for
ethyl glucuronide (EtG), an alcohol metabolite with a 3-day detection period (McDonell et
al., 2012).
The final study in this trend was a feasibility study demonstrating that CM was effective at
increasing adherence to a schedule of regular reporting of cocaine cravings/use using an
interactive voice response (IVR) system (Lindsay et al., 2014).
3.2.5. Combining CM with pharmacotherapies—Combining CM with
pharmacotherapies is the fourth largest trend in the review (Table 3). Eight of the 69
included studies (11.5%) were categorized under this trend, with five (62.5%) targeting
abstinence, and three (37.5%) targeting another therapeutic goal. Six studies (75%) reported
significant during-treatment effects. An effect size could be calculated for five of those six
studies, with an average of 1.03 (95% CI: 0.69, 1.36). Average during-treatment effect size
for seven of the eight studies categorized under this trend was 0.67 (95% CI: 0.41, 0.92).
Only one of the six studies reporting significant during-treatment effect assessed outcomes
following discontinuation of an efficacious during-treatment incentives intervention, and
treatment effects were not maintained; the effect size was 0.41 (95% CI: −0.53, 1.35) (Gray
et al., 2011). There were a total of two studies within this trend that included one or more
follow-up assessments, with an average effect size of 0.21 (95% CI: −0.35, 0.76).
The directions taken by studies included in this trend were: (1) evaluating effects of CM and
pharmacotherapy alone versus in combination (Gray et al., 2011; Tidey et al., 2011;
Winstanley et al., 2011; Umbricht et al., 2014), (2) assessing the addition of CM to a
pharmacotherapy to sustain treatment effects (Ling et al., 2013), and (3) reinforcing
pharmacotherapy adherence using CM (DeFulio et al., 2012; Everly et al., 2011; Dunn et al.,
2013).
Two of the four studies that evaluated the independent versus combined effects of
pharmacotherapy plus CM targeted cigarette smoking. In Gray et al. (2011), the combination
of bupropion plus CM more effectively reduced adolescent cigarette smoking than
bupropion or CM alone. In contrast, combining bupropion with CM was no more effective
than CM alone in smokers with schizophrenia (Tidey et al., 2011). The other two studies of
this type targeted cocaine use among opiate-dependent participants. In Winstanley et al.
(2011), CM alone was more effective in reducing cocaine use relative to fluoxetine alone or
CM plus fluoxetine together, whereas in Umbricht et al. (2014) neither the combination of
CM plus topiramate nor either of these treatment components alone reduced cocaine use.
Thus, with the exception of adolescent smokers, combining CM with pharmacotherapy did
not improve outcomes in these studies, which is not encouraging but also not surprising in
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that in at least two of the studies there was little evidence that the pharmacotherapy alone
was efficacious for the SUD investigated (Umbricht et al., 2014; Winstanley et al., 2011).
To further examine CM in opiate-dependent patients, Ling et al. (2013) compared outcomes
among those receiving buprenorphine in combination with standard drug abuse counseling
only or in combination with CM, CBT, or CM and CBT. Outcomes across the four treatment
conditions did not differ significantly, which is inconsistent with an extensive literature
demonstrating that CM improves outcomes above opioid substitution therapy and drug abuse
counseling only (e.g., Silverman et al., 1996a; Silverman et al., 1996b).
The remaining three studies demonstrated that employment-based reinforcement promotes
adherence to extended-release (Defulio et al., 2012; Dunn et al., 2013; Everly et al., 2011)
and oral naltrexone (Dunn et al., 2013), an opioid antagonist that can decrease opiate use but
for which adherence is typically poor.
3.2.6. Investigating longer-term outcomes—Although 28 of the studies with
significant treatment effects in the current review conducted follow-up assessments to
evaluate post-treatment outcomes, the five studies (7.2%) categorized under this trend were
those explicitly focused on investigating longer-term outcomes as a primary aim (Table 4).
Three of these five studies (60%) targeted abstinence and two (40%) targeted abstinence and
another therapeutic outcome. Four studies (80%) reported significant during-treatment
effects with an average effect size of 0.52 (95% CI: 0.19, 0.86). The one study that failed to
produce a significant during-treatment effect also failed to include sufficient information to
calculate an effect size. Thus, the overall effect size is the same as the one reported above for
studies producing significant effects.
All four studies that produced significant treatment effects also measured outcomes
following discontinuation of the incentives, with two (50%) of those studies maintaining
significant effects at follow-up assessments with an average effect size of 0.79 (95% CI:
0.35, 1.22). Three of those four studies (two that produced significant follow-up effects and
one that did not) included sufficient information to calculate a post-treatment effect size with
an overall average of 0.49 (95% CI: 0.02, 0.97).
Studies in this trend took one of two directions. The first direction involved comparing
outcomes when CM was administered alone versus combined with another treatment that
might be expected to increase during-treatment abstinence. Wang et al. (2014) reported that
supplementing methadone maintenance therapy (MMT) with CM failed to reduce heroin use
and incidences of HIV infection relative to MMT alone (Wangetal., 2014). A second study
reported that combining CM with the community reinforcement approach (CRA) improved
post-treatment cocaine abstinence rates and general psychosocial functioning relative to
standard care (Secades-Villa et al., 2011). The other two studies evaluated CM combined
with CBT. In one of those studies, combining CM with CBT produced better cocaine use
outcomes than standard care or CBT alone (McKay et al., 2010), whereas in the other study
combining CM with CBT failed to decrease longer-term cannabis use significantly more
than CBT or CM alone (Carroll et al., 2012). These discrepant findings with respect to
outcomes when combining CM and CBT are difficult to reconcile as the targeted drugs
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(cocaine vs. marijuana) and CM schedule arrangements differed considerably. However, it
does merit mention that the negative findings reported by Carroll et al. regarding the
combined effects of CM and CBT compared to CM delivered alone is consistent with other
research on this particular treatment combination (e.g., Petitjean et al., 2014; Rawson et al.,
2002; Rawson et al., 2006).
The second direction taken involved only one study and used an arrangement wherein
opioid-dependent individuals earned access to paid employment contingent on cocaine
abstinence. This therapeutic workplace sustained cocaine abstinence throughout one year of
abstinence-contingent employment, but treatment effects were not maintained following
discontinuation of the abstinence contingency (DeFulio and Silverman, 2011).
3.2.7. Using CM as a research tool—Using CM as a research tool is one application
that currently is quite underutilized (Table 4). This refers to using CM to experimentally
manipulate drug use or other targets in order to answer other research questions (e.g., how a
period of initial abstinence influences the probability of longer-term abstinence, or how
regular use of a novel pharmacotherapy alters illicit drug use). Only two of the 69 studies
(2.8%) in the current review were categorized under this trend (Bradstreet et al., 2014; Kurti
and Dallery, 2014). Both reported significant treatment effects. An effect size could be
calculated for one of the studies, which was 3.27 (95% CI: 2.1, 4.45). Neither examined
post-treatment outcomes.
Bradstreet et al. (2014) used CM to promote differential levels of recent abstinence from
cigarette smoking in order to experimentally examine impacts on cue-induced craving and
response inhibition in a Go/No-Go task. Abstinence across a two-week period produced
statistically significant and robust decreases in generalized craving relative to 1–2 days of
abstinence, although no differences in cue-induced craving or response inhibition were
noted. Kurti and Dallery (2014) administered CM in a laboratory setting to examine whether
a laboratory-based model of this treatment combined with physical exercise had greater
impacts on craving and smoking relative to exercise or CM alone. No significant differences
were noted between combined CM and exercise versus CM alone.
Future research that uses CM as a research tool has the potential to contribute new
knowledge about numerous important aspects of SUDs, including drug use impacts on
epigenetic profiles, brain function and structure, immune function, and other health
outcomes impacted by SUDs.
4. Discussion
Over the past 5.2 years, voucher-based CM studies have continued to appear in the literature
at a healthy pace of 13.3 studies per year, which is directly comparable to the 14.4 studies
per year across the five years covered by the immediately prior review (Higgins et al., 2011).
Considered together, the number of CM studies published per year over the past decade
represents a substantial increase beyond the rate of 3.0 studies per year (4.2 per year if
studies that would have met inclusion criteria in the present review are included) reported in
Lussier et al. (2006), which covered the years 1991–2004. In addition to consistency in the
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pace with which CM studies have been published over the past ten years, the efficacy of CM
treatments have remained consistent as well, with the current and prior reviews reporting an
86% and 88% efficacy rate, respectively. Collapsing across the three reviews, this represents
176 controlled studies on the use of voucher-based CM with substance users, with 151
(86%) of those studies supporting efficacy. For studies reporting significant treatment effects
while the incentives were in place, average effect size was moderate to large, and for those
studies where significant treatment effects remained significant at one or more follow-ups
following discontinuation of the incentives intervention average effect size was small. The
same is true for effect sizes based on all studies assessing during-treatment effects and all
studies assessing post-treatment effects. By any standard for evaluating development of
treatments for SUDs that we are familiar with, that is a striking level of empirical support for
efficacy. Indeed, this empirical support has led the National Institute on Clinical Excellence
to recommend nationwide adoption of CM for intensive outpatient treatments for illicit drug
use disorders in the U.K. (Pilling et al., 2007) and more recently a similar action within the
U.S. Veteran Administration hospital system (Petry et al., 2014b).
Also consistent across the present and immediately prior reviews are the trends into which
studies can be categorized, with two exceptions: (1) the trend
extending the intervention to
additional SUDs
was removed in the current review as no new SUDs were targeted in this
review period, and (2) the addition of the trend
incorporating new technology into
CM was
new in the current review. There was also consistency across the current and immediately
prior reviews with regards to substantial focus on use of CM with special populations. This
appears to be where CM is clearly finding a niche, perhaps because CM has been
demonstrated to be among the most effective treatments at promoting abstinence in these
populations. In comprehensive meta-analyses on smoking-cessation interventions for
pregnant women, for example, CM produces larger treatment effects than any intervention
tested in controlled trials going back to 1985 (see Higgins and Solomon, 2016).
In the two prior reviews by our group, we identified several priorities for future CM
research. In Lussier et al. (2006), we called for increased evaluation of intervention duration
and voucher incentive value. Studies in the current review that evaluated these parameters
support earlier findings that longer duration of treatment and higher value incentives
moderate treatment efficacy. In the immediately prior review (Higgins et al., 2011), we
called for the development of novel monitoring technologies to facilitate frequent and
accurate monitoring of alcohol intake so that CM could be used with this highly prevalent
SUD and also for extending CM to individuals residing in geographically remote areas. The
development of remote monitoring devices such as the SCRAM bracelet and internet and
smartphone arrangements for monitoring breath alcohol and carbon monoxide levels within
the past 5.2 years has contributed to the substantial progress evident in this review (also see
Kurti et al., 2016). The use of urine EtG monitoring also holds promise for extending CM to
alcohol use disorders (McDonell et al., 2012).
Another priority identified in both of the prior reviews was further examination of CM
effects on longer-term abstinence and other post-treatment outcomes. The proportion of
studies that included ≥ one follow-up assessment in the present review (47%) increased
slightly above the proportion that included follow-ups in the prior review (41%). In addition,
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the proportion of studies supporting the maintenance of treatment effects following the
discontinuation of incentives also appears to have increased between reviews, from 21% in
the immediately prior review to 29% in the current review. Clearly there is a weakening in
the magnitude of treatment effect size when comparing overall effects while incentives are in
place compared to overall effects after their discontinuation, with generally moderate to
large effect sizes observed during the former (i.e., 0.62 (95% CI: 0.54, 0.70)) and small
effect sizes in the latter (i.e., 0.26 (95% CI: 0.11, 0.41)). Others have noted this trend toward
more CM studies examining the sustainability of post-intervention treatment effects as well
(McKay et al., 2010), but more needs to be done in terms of moving in the direction of all
CM studies including follow-up assessments and increases in the quality of these efforts. As
CM studies increasingly focus on longer-term behavior change, it will be important for
researchers to consider carefully how to program for the maintenance of treatment effects
using naturalistic or more contrived reinforcers for healthy living. Note that in the small
handful of studies that were explicitly focused on promoting longer-term outcomes (Table
4), average overall effect size was 0.49 (95% CI: 0.02, 0.97). Combined treatment
interventions that increase during-treatment abstinence and provide skills for sustaining
abstinence may extend the duration of abstinence post-treatment (McKay et al., 2010;
Higgins et al., 2000), as might employment-based workplace contingencies or other
comparable arrangements that are designed to keep programmed contingencies for
reinforcing abstinence in place long-term or chronically (Silverman et al., 2012). Important
to underscore is that this need for greater focus on promoting and sustaining longer-term
behavior change is an important priority and challenge for CM. Also important to note,
however, is that the need for greater focus on sustaining behavior change is not unique to
CM and extends to all behavioral, psychosocial, and pharmacological interventions for
SUDs and other chronic conditions (e.g., obesity) where behavior is a proximal cause.
As development of CM continues, it will be important to remain sensitive to the importance
of parameters that impact efficacy and effect size (e.g., short delays between engaging in the
target behavior and earning reinforcement, larger incentive values, and longer duration
interventions). Implementing lower-cost CM and/or less intensive CM interventions (e.g.,
shorter intervention durations, less frequent behavior monitoring) may increase the
likelihood of adoption by community clinics with fewer resources, etc., but such
modifications can be expected to reduce treatment effect size as well. To simultaneously
maintain the efficacy of CM interventions while promoting greater dissemination, it will be
necessary for future research to integrate the findings from existing research (e.g., adherence
to the parameters revealed to be critical to treatment efficacy in experimental evaluations)
with new developments (e.g., incorporating technology in CM). Most importantly, cost-
effectiveness should be the eventual arbiter in such matters, but is an area where CM and
other treatment development research for SUDs is lacking. In a recent review on cost-
effectiveness studies on use of CM with illicit drug use disorders, for example, only nine
studies were identified (Shearer et al., 2015). While results were generally supportive, they
were also deemed inconclusive. We consider greater attention to that gap to be a high
priority in future CM research along with continued attention to longer-term outcomes
discussed above, especially studies wherein preparation for maintenance of treatment effects
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was part of the during-treatment intervention or programmed contingencies of reinforcement
for healthy behavior change are sustained longer-term.
In sum, CM continues to be a highly efficacious intervention that produces large to medium
effects during treatment and follow-up, respectively, and across a wide variety of SUDs,
populations, and settings. Further dissemination of CM into the public and private sector has
substantial promise for improving public health by promoting abstinence and related
therapeutic changes among those struggling with SUDs. Further development of this
intervention model will be enhanced by greater attention to promoting and sustaining longer-
term change, strategies to increase treatment reach through remote monitoring, and careful
examination of impacts on health, quality-of-life, and societal (e.g., crime, economic
productivity) outcomes and cost-effectiveness.
Acknowledgments
Funding
This project was supported in part by Research Grants R01HD075669 and R01HD078332 from the National
Institute of Child Health and Human Development, Center of Biomedical Research Excellence award
P20GM103644 from the National Institute of General Medical Sciences, and Institutional Training Award
T32DA07242 from the National Institute on Drug Abuse. The funding sources had no other role in this project
other than financial support.
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Fig. 1.
Cumulative plot of the number of citations identified in a PubMed search of the term
‘contingency management’ involving substance use disorders (SUDs). The search included
all citations through May of 2015.
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Table 1
Extending the intervention to special populations.
Study Primary trend Additional trends nCM duration (weeks) Maximum earnings Statistically
significant
treatment
effect
Included follow up? Statistically
significant
effects at
follow-up
Drug of abuse CM target
Alessi and
Petry (2014) Special populations Community clinics 45 4 CNBD Yes Yes No Nicotine Abstinence
Businelle et
al. (2014) Special populations N/A 68 5 $150 Yes No N/A Nicotine Abstinence
Drummond
et al. (2014) Special populations N/A 100 24 $225 Yes No N/A Nicotine Abstinence
Dunn et al.
(2010) Special populations Pharmacotherapy 40 2 $362.50 Yes Yes No Nicotine Abstinence
García-
Fernández
et al. (2013)
Special populations N/A 108 24 $2196.00 Yes No N/A Cocaine Abstinence
Hagedorn et
al. (2013) Special populations Community clinics 332 8 CNBD Yes Yes No Polydrug Abstinence
Hertzberg et
al. (2013) Special populations Technology 22 4 $690 (530 for CM) No N/A N/A Nicotine Abstinence
Higgins et
al. (2014) Special populations Parametric questions, longer-
term outcomes 130 52 $1180 Yes Yes No Nicotine Abstinence
Holtyn et al.
(2014) Special populations N/A 98 30 $6000 Yes Yes No Cocaine/Opioids Abstinence + other
therapeutic goal
Kaminer et
al. (2014) Special populations N/A 79 7 CNBD No N/A N/A Marijuana Abstinence
Kelly et al.
(2014) Special populations Community clinics 160 6 CNBD Yes No N/A Polydrug Other therapeutic goal
Kendzor et
al. (2014) Special populations Pharmacotherapy 146 4 $150 Yes Yes Yes Nicotine Abstinence
Kidorf et al.
(2013) Special populations Community clinics 125 12 $300.00 Yes No N/A Opioids Other therapeutic goal
Krishnan-
Sarin et al.
(2013)
Special populations N/A 157 4 $262 Yes Yes No Nicotine Abstinence
McDonell et
al. (2013) Special populations Community clinics 176 12 CNBD Yes Yes Yes Polydrug Abstinence
Menza et al.
(2010) Special populations N/A 127 12 $453.75 No N/A N/A Methamphetamine Abstinence
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Study Primary trend Additional trends nCM duration (weeks) Maximum earnings Statistically
significant
treatment
effect
Included follow up? Statistically
significant
effects at
follow-up
Drug of abuse CM target
Ondersma
et al. (2012) Special populations Community clinics,
parametric, technology 110 10 $50.00 No N/A N/A Nicotine Abstinence
Petry et al.
(2010) Special populations Community clinics, parametric 170 24 CNBD Yes Yes No Cocaine/opioids Other therapeutic goal
Petry et al.
(2013) Special populations Community clinics 19 8 CNBD Yes No N/A Cocaine Abstinence
Reback et
al. (2010) Special populations Community clinics 131 24 CNBD Yes Yes Yes Polydrug Abstinence + other
therapeutic goal
Schottenfeld
et al. (2011) Special populations Longer-term outcomes 145 24 $935 Yes Yes Yes Cocaine Abstinence
Secades-
Villa et al.
(2013)
Special populations Community clinics 118 24 $2196.00 Yes No N/A Cocaine Abstinence
Stanger et
al. (2009) Special populations N/A 69 12 $570 Yes Yes No Marijuana Abstinence
Note: primary trend refers to main trend authors assigned study to; additional trends refers to additional trends study was assigned to aside from the primary trend;
n
refers to sample size (across all groups);
CM duration refers to the number of weeks during which contingent incentives could be earned (CNBD = duration could not be determined); Maximum earnings refers to the maximum amount that could
be earned in the intervention (CNBD = maximum earnings could not be determined); Statistically significant treatment effects and effects at follow-up defined in all studies as outcomes significant at p <
0.05; Follow up effects were only evaluated if during treatment effects were statistically significant; drug of abuse refers to drug targeted by CM intervention; CM target refers to the behavior on which
incentives were contingent.
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Table 2
Investigating parametric questions and extending the intervention into community clinics.
Study Primary trend Additional trends nCM duration (weeks) Maximum earnings Statistically
significant
treatment
effect
Included follow up? Statistically
significant
effects at
follow-up
Drug of abuse CM target
Festinger et
al. (2014) Parametric N/A 222 12 CNBD Yes No N/A Cocaine Abstinence
Kirby et al.
(2013) Parametric N/A 130 36 CNBD Yes No N/A Cocaine Abstinence
Lamb et al.
(2010) Parametric N/A 146 12 $1157.50 Yes No N/A Nicotine Abstinence
Ledgerwood
et al. (2014) Parametric N/A 81 3 CNBD Yes Yes No Nicotine Abstinence
Packer et al.
(2012) Parametric N/A 103 1 $207.50 Yes No N/A Nicotine Abstinence
Petry et al.
(2012c) Parametric Community clinics 442 12 $560 Yes Yes No Cocaine Abstinence + other
therapeutic goals
Petry et al.
(2014a) Parametric Community clinics 240 12 $900 Yes Yes No Cocaine Abstinence
Tuten et al.
(2012) Parametric Special populations 133 13 $1364 No N/A N/A Cocaine/Opioids Abstinence
Roll et al.
(2013) Parametric N/A 118 16 $500.00 Yes Yes Yes Methamphetamine Abstinence
Romanowich
and Lamb
(2010)
Parametric N/A 57 3 $100.00 Yes Yes No Nicotine Abstinence
Romanowich
and Lamb
(2013)
Parametric N/A 30 1 $375.00 Yes Yes No Nicotine Abstinence
Branson et
al. (2012) Community clinics Special populations 52 3 CNBD Yes No N/A Polydrug Other therapeutic goals
Chen et al.
(2013) Community clinics N/A 246 12 CNBD Yes No N/A Opioids Abstinence + other
therapeutic goal
García-
Fernández et
al. (2011)
Community clinics N/A 68 24 $2196.00 Yes No N/A Cocaine Abstinence
Hser et al.
(2011) Community clinics N/A 319 12 CNBD Yes No N/A Opioids Abstinence + other
therapeutic goal
Jiang et al.
(2012) Community clinics N/A 160 12 $435.80 No N/A N/A Opioids Abstinence + Other
Therapeutic Goal
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Study Primary trend Additional trends nCM duration (weeks) Maximum earnings Statistically
significant
treatment
effect
Included follow up? Statistically
significant
effects at
follow-up
Drug of abuse CM target
Killeen et al.
(2012) Community clinics Special populations 31 10 CNBD No N/A N/A Marijuana Abstinence
Petitjean et
al. (2014) Community clinics N/A 60 4 CNBD Yes Yes No Cocaine Abstinence
Petry et al.
(2011) Community clinics N/A 239 12 CNBD Yes Yes No Polydrug Abstinence + other
therapeutic goals
Petry et al.
(2012a) Community clinics N/A 130 12 CNBD Yes Yes No Cocaine Abstinence
Petry et al.
(2012b) Community clinics N/A 43 12 CNBD Yes No N/A Polydrug Abstinence
Secades-
Villa et al.
(2014)
Community clinics N/A 92 6 $412 Yes Yes Yes Nicotine Abstinence
Walker et al.
(2010) Community clinics N/A 90 10,14 CNBD Yes No N/A Polydrug Other therapeutic goals
Note: primary trend refers to main trend authors assigned study to; Additional trends refers to additional trends study was assigned to aside from the primary trend;
n
refers to sample size (across all groups);
CM duration refers to the number of weeks during which contingent incentives could be earned (CNBD = duration could not be determined); Maximum earnings refers to the maximum amount that could
be earned in the intervention (CNBD = maximum earnings could not be determined); Statistically significant treatment effects and effects at follow-up defined in all studies as outcomes significant at p <
0.05; Follow up effects were only evaluated if during treatment effects were statistically significant; drug of abuse refers to drug targeted by CM intervention; CM target refers to the behavior on which
incentives were contingent.
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Table 3
Incorporating new technologies in CM AND combining pharmacotherapies with CM.
Study Primary trend Additional trends nCM duration (weeks) Maximum earnings Statistically
significant
treatment
effect
Included follow up? Statistically
significant
effects at
follow-up
Drug of abuse CM target
Alessi and
Petry
(2013)
Technology N/A 30 4 $340 Yes No N/A Alcohol Abstinence
Barnett et
al. (2011) Technology N/A 13 2 $154 Yes No N/A Alcohol Abstinence
Dallery et
al. (2013) Technology N/A 77 7 $530 Yes Yes No Nicotine Abstinence
Dougherty
et al.
(2014)
Technology N/A 26 8 $300 Yes No N/A Alcohol Abstinence
Lindsay et
al. (2014) Technology N/A 57 2 CNBD Yes No N/A Cocaine Other therapeutic goal
McDonell
et al.
(2012)
Technology N/A 15 4 $96 Yes No N/A Alcohol Abstinence
Meredith
and
Dallery
(2013)
Technology N/A 32 2 $56.25 Yes No N/A Nicotine Abstinence
Meredith
et al.
(2011)
Technology N/A 13 2 $161.50 Yes No N/A Nicotine Abstinence
Defulio et
al. (2012) Pharmacotherapy N/A 38 24 CNBD Yes No N/A Opioids Other therapeutic goal
Dunn et al.
(2013) Pharmacotherapy Special populations 67 26 CNBD Yes No N/A Opioids Other therapeutic goal
Everly et
al. (2011) Pharmacotherapy N/A 35 26 CNBD Yes No N/A Opioids Other therapeutic goal
Gray et al.
(2011) Pharmacotherapy Special populations 134 6 $275 Yes Yes No Nicotine Abstinence
Ling et al.
(2013) Pharmacotherapy N/A 202 16 $1460.00 No N/A N/A Opioids Abstinence
Tidey et al.
(2011) Pharmacotherapy Special populations 57 3 $350 Yes No N/A Nicotine Abstinence
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Study Primary trend Additional trends nCM duration (weeks) Maximum earnings Statistically
significant
treatment
effect
Included follow up? Statistically
significant
effects at
follow-up
Drug of abuse CM target
Umbricht
et al.
(2014)
Pharmacotherapy N/A 171 12 $1155.00 No No N/A Cocaine Abstinence
Winstanley
et al.
(2011)
Pharmacotherapy N/A 145 12 $1155.00 Yes No N/A Cocaine Abstinence
Note: primary trend refers to main trend authors assigned study to; Additional trends refers to additional trends study was assigned to aside from the primary trend;
n
refers to sample size (across all groups);
CM duration refers to the number of weeks during which contingent incentives could be earned (CNBD = duration could not be determined); Maximum earnings refers to the maximum amount that could
be earned in the intervention (CNBD = maximum earnings could not be determined); Statistically significant treatment effects and effects at follow-up defined in all studies as outcomes significant at p <
0.05; Follow up effects were only evaluated if during treatment effects were statistically significant; drug of abuse refers to drug targeted by CM intervention; CM target refers to the behavior on which
incentives were contingent.
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Author Manuscript Author Manuscript Author Manuscript Author Manuscript
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Table 4
Investigating longer-term outcomes and trends using CM as a research tool.
Study Primary trend Additional trends nCM duration (weeks) Maximum earnings Statistically
significant
treatment
effect
Included follow up? Statistically
significant
effects at
follow-up
Drug of abuse CM target
Carroll et
al. (2012) Longer-term outcomes Special
populations,
community clinics
127 12 $250 Yes Yes No Marijuana Abstinence
+ other
therapeutic
goals
DeFulio
and
Silverman
(2011)
Longer-term outcomes N/A 51 52 CNBD Yes Yes No Cocaine Abstinence
McKay et
al. (2010) Longer-term outcomes N/A 100 12 $1150.00 Yes Yes Yes Cocaine Abstinence
Secades-
Villa et
al. (2011)
Longer-term outcomes N/A 64 52 $1111.58 Yes Yes Yes Cocaine Abstinence
Wang et
al. (2014) Longer-term outcomes Community clinics 2662 24 CNBD No N/A N/A Opioids Abstinence
+ other
therapeutic
goals
Bradstreet
et al.
(2014)
As a research tool N/A 34 2 $507.50 Yes No N/A Nicotine Abstinence
Kurti and
Dallery
(2014)
As a research tool N/A 20 4 CNBD Yes No N/A Nicotine Abstinence
Note: primary trend refers to main trend authors assigned study to; Additional trends refers to additional trends study was assigned to aside from the primary trend;
n
refers to sample size (across all groups);
CM duration refers to the number of weeks during which contingent incentives could be earned (CNBD = duration could not be determined); Maximum earnings refers to the maximum amount that could
be earned in the intervention (CNBD = maximum earnings could not be determined); Statistically significant treatment effects and effects at follow-up defined in all studies as outcomes significant at p <
0.05; Follow up effects were only evaluated if during treatment effects were statistically significant; drug of abuse refers to drug targeted by CM intervention; CM target refers to the behavior on which
incentives were contingent.
Prev Med
. Author manuscript; available in PMC 2017 April 08.