..

119© NAPICU 2016

Journal of Psychiatric
Intensive Care

Journal of Psychiatric Intensive Care, 12 (2): 119–127
doi:10.20299/jpi.2016.009
Received 15 July 2015 | Accepted 28 January 2016
© NAPICU 2016

REVIEW ARTICLE

The use of a token economy for

behaviour and symptom management

in adult psychiatric inpatients: a critical

review of the literature

Krista Glowacki, Grace Warner, Cathy White

School of Occupational Therapy, Dalhousie University, Canada
Correspondence to: Krista Glowacki, School of Occupational Therapy, Forrest
Building, PO Box 15000 Halifax, Nova Scotia, B3H 4R2, Canada;
[email protected]

Background: A token economy is a behavioural modification and reward
based intervention in which tokens are given for predefined terms. This
review aims to answer the question: What is the effectiveness of the use of
a token economy for the reduction of negative behaviours and symptoms
in adult psychiatric inpatients?
Method: A systematic review of studies using a token economy for adults
with mental illness, within an inpatient setting was undertaken for the
period 1999–2013. References cited in relevant literature were also
examined.
Results: The Oxford CEBM Levels of evidence was used to determine
quality. Grade A and B recommended studies were included in the review.
A total of seven studies were included in the analysis. All of the studies
showed the effectiveness of a token economy for reducing negative
behaviours and symptoms in the short-term.
Conclusions: The use of a token economy, on the basis of reward and
encouragement, should be considered within inpatient psychiatric settings.
The literature shows the effectiveness on behavioural changes in reduction
of violence and aggression. The literature on negative symptom reduction
is scarce and cannot be generalised. There is no evidence to support the
transfer outside of an inpatient/secure setting.

Key words: token economy; psychiatric inpatient; symptom manage-
ment; behaviour management

Financial support: This research received no specific grant from any funding agency,
commercial or not-for-profit sectors.
Declaration of interest: None.

120 © NAPICU 2016

GLOWACKI ET AL.

Introduction

With the shift toward community-based mental health
care, inpatient psychiatry units are seeing an increase in
acuity of the patients who come through their doors (Bow-
ers, 2005). Common reasons for admission include danger
to self or others, severe mental disorder such as psychosis,
and extreme behaviours such as agitation, mania,
unpredictability, confusion, disorientation, emotional
lability, distress/tears, acting out and delusions (Bowers,
2005). Patients may exhibit negative symptoms such as
slow and superficial responses, social withdrawal, and
lack of energy (Hopko et al. 2003; Gholipour et al. 2012),
or negative behaviours, including agitation and aggres-
sion particularly toward staff members (Lepage et al.
2003; Park & Lee, 2012). Thus, the creation of a safe and
secure environment becomes paramount.

As Bowers (2005) discussed, ongoing risk assessment
and monitoring and observation of the patients are routine
aspects of the care, which may lead to the need to employ
skills in negotiation, persuasion, coaxing, distraction and
de-escalation. When patients do escalate, disrupting the
milieu and placing themselves and/or others at risk, be-
haviour management strategies such as exerting physical
control, restraints and coercive use of medications may be
employed to mediate the behaviour. One approach to
behaviour modification that has received limited recent
attention in the literature is the use of a token economy.

Background

A token economy, developed for use within inpatient
psychiatry settings, is a behaviour modification interven-
tion that can be used to shape behaviours including acquir-
ing new skills, reducing undesired behaviours, increasing
treatment compliance, and improving overall manage-
ment of patients on psychiatry units (LePage et al. 2003;
McMonagle & Sultana, 2000). This intervention is based
on operant conditioning. Patients can earn ‘tokens’ which
have no innate value, and can exchange them for some-
thing that does have value to them, such as goods, services
or privileges in the facility when they exhibit a desired
behaviour (Seegert, 2003; McMonagle & Sultana, 2000).
The first principle of the token economy is the law of cause
and effect based on the idea that reinforcement is the most
effective means in changing behaviour. The second princi-
ple is the law of contiguity association, in that two events
will be associated with one another if they happen together
(Dickerson et al. 2005; McMonagle & Sultana, 2000). In
the original economy, both reward and punishment tech-
niques could be implemented (Kreyenbuhl et al. 2010).
Punishment is now viewed as inappropriate within a
healthcare setting, causing the decline of this intervention.
Punishment is considered a negative consequence, includ-

ing the removal of tokens. There are common mis-
conceptions about all token economies, including the
belief that the intervention is abusive, it does not foster
individual treatment, and does not generalise. These mis-
conceptions prevail among health care practitioners and
further contribute to its lack of use (LePage et al. 2003).

A token economy can facilitate improvement in behav-
iour and function. It is an economically friendly
intervention, and can be beneficial in facilities with lim-
ited resources (LePage, 1999; Seegert, 2003; Coelho et al.
2008; Comaty et al. 2001; McMonagle & Sultana, 2000;
Kreyenbuhl et al. 2010). It is relatively simple in its overall
conceptualisation for those involved, and is beneficial for
reducing challenging or disruptive behaviours (LePage,
1999; Coelho et al. 2008). Token economies can be used to
increase functioning and to foster recovery, a key focus of
today’s mental health care (Hassell, 2009).

A systematic review of the use of token economies was
published in 2000, analysing literature up to 1999
(McMonagle & Sultana, 2000). McMonagle & Sultana
(2000) concluded by recommending the token economy
as a cost-effective alternative to psychosocial interven-
tions in institutions with financial struggles. The article
also recommends further in-depth research in a controlled
setting using randomised trials to further explore effec-
tiveness. This systematic review of the literature examines
current research (1999–2013) on the use of a current token
economy in adult inpatient psychiatric settings. The ques-
tion guiding the review is: What is the effectiveness of the
use of a token economy for the reduction of negative
behaviours and symptoms in adult psychiatric inpatients?

Method

Inclusion criteria

Types of studies. Peer reviewed articles including:
randomised controlled trials, prospective cohort studies,
retrospective cohort studies and pre–post design.

Types of participants. Adults ages 18 and older admitted
to a psychiatric facility as an inpatient in a forensic, acute,
or rehabilitation unit, with a mental health disorder as
identified in the Diagnostic Statistical Manual of Mental
Disorder, 5th Edition (DSMV).

Types of interventions. Intervention included a token
economy in which tokens or vouchers are given as rewards
for behaviour specified prior to entering the programme/
economy. Rewards may be given for positive behaviour or
abstinence of negative behaviour. The goal is to achieve
behavioural change by means of use of non-monetary and
non-consumable tokens, which can be exchanged for a
variety of goods, privileges or services in the facility.

121© NAPICU 2016

A TOKEN ECONOMY

Types of outcome measures. To determine if the therapy is
effective, there must be a reduction in one of the two
identified outcomes after the implementation of the inter-
vention. The identified outcomes are negative behaviours
or negative symptoms. Negative behaviours include: vio-
lence, aggression, and drug abuse. Negative symptoms
include: flat affect, lack of pleasure in life, lack of partici-
pation, lack of ability to begin and sustain activities, and
lack of socialisation and interaction with others. Out-
comes can be measured by observation data, frequency
data, incident reports, patient charts, group participation
numbers/percentages and number of positive urine sam-
ples. Statistical information was extracted from each study
inclusive of average test scores and standard deviation,
statistical significance and effect size in changes or differ-
ences.

Search strategy

Electronic searches were undertaken, limiting results to
the English language and publication in the period 1999–
2013 (due to the McMonagle & Sultana (2000) review
including research prior to 1999). The databases CINAHL,
EMBASE, OTseeker, PubMed, PsycInfo and Google
Scholar were used. The search terms used in CINAHL
(EBSCOhost) were: (1) “token economy” OR (tokens OR
vouchers) and psychiatric OR (mental* N2 (health OR ill*

OR disorder*)) and inpatient* OR hospital* OR ward*
OR unit OR patient* OR forensic*; (2) “token economy”
OR tokens OR vouchers and psychiatric OR mental*
NEAR/2 (health OR ill* OR disorder*) and inpatient* OR
hospital* OR ward* OR unit OR patient* OR forensic* and
behavi* OR violen* OR aggressi* OR negative; (3) “To-
ken economy” and adult; (4) “Token economy” and
psychiatric OR (mental* N2 (health OR ill* OR disor-
der*)); (5) Voucher-based and mental health. Other similar
search terms were used in the other databases. An exami-
nation of references cited in relevant literature was also
undertaken.

Exclusion criteria

Research done before 1999, participants under the age of
18, outpatient settings, and diagnoses not in the DSMV
were excluded. Specific study types not included were:
systematic reviews, open forum blogs, hospital unit re-
views and descriptive articles of intervention without a
measureable outcome (see Fig. 1).

Data extraction & quality review

Articles were identified through electronic searches and
abstracts were reviewed. Those that did not meet the
inclusion criteria were then excluded. Of the abstract
reviews, 20 articles were identified and the full manuscripts

Identified through

searching database:

n = 342

Identified through

examination of

references:

n = 3

Excluded after

abstract review:

n = 325

Full-text articles assessed

for eligibility:

n = 20

Included after

manuscript review:

n = 7

Excluded after manuscript review: n = 13

Reasons: Population not inpatients: n = 4

Study design: n = 7

Outcome measured not negative behaviour change: n = 1

Full text not accessible: n = 1

Fig. 1. Articles included and excluded.

122 © NAPICU 2016

GLOWACKI ET AL.

of the papers were read and assessed for quality and
eligibility. The Oxford Centre for Evidence-Based Medi-
cine Levels of Evidence was used to determine quality,
and only grade A and B studies were included in the review
(OCEBM, 2009). Grade A studies are considered the
highest quality and grade B studies are the second highest
quality. After the full manuscript reviews, 13 were ex-
cluded, leaving 7 studies to be included in the systematic
review. Figure 1 indicates reasons why studies were ex-
cluded.

Results

Data were extracted from seven studies and compared to
determine the effectiveness of a token economy (Table 1).
Each study included a rewards and incentive based token
economy for adults within an inpatient psychiatry setting.
Of the seven studies, three were randomised controlled
trials, one was a prospective cohort, two were pre–post
designs and one was a retrospective cohort. All of these
studies were categorised using the OCEBM (2009) to
determine study quality.

The studies classified as grade A of the OCEBM (2009)
were the three randomised controlled trials (Hopko et al.
2003; Gholipour et al. 2012; Park & Lee, 2012).
Randomisation methods were difficult to assess as entire
inpatient units were used. Two of the studies looked only at
male units and were done outside of North America
(Gholipour et al. 2012; Park & Lee, 2012), and in one, all
males on the unit were diagnosed with schizophrenia
(Gholipour et al. 2012). Thus, cultural and gender differ-
ences should be considered and the results be used with
caution to generalise to North American culture and prac-
tice, and to mixed units. Two of the studies had small
sample sizes (Hopko et al. 2003; Gholipour et al. 2012).
The next grade of studies, level B of the OCEBM (2009),
were pre–post designs and prospective cohorts (LePage,
1999; Comaty et al. 2001; LePage et al. 2003). Two of the
studies only analysed one unit of a hospital, limiting the
generalisation, and there was no control group as all patients
in the unit participated in the token economy (LePage,
1999; LePage et al. 2003). Lastly, a retrospective study,
also grade B of the OCEBM (2009) was used (Hassell,
2009). This included an analysis of medical records and no
power analysis was used to determine if sample size was
adequate. In the same study various healthcare profession-
als implemented the token economy and no information
was given on inter-relater reliability or training. It should
also be noted that in all of the studies the intervention was
combined with individualised pharmacological treatment.

The effect of interventions on outcomes

The effectiveness of the intervention being analysed will
be reported as a narrative synthesis. This part will be

separated into two sections. The first will describe the
effectiveness of the intervention on negative behaviours,
and the second will describe the effectiveness on negative
symptoms.

Behaviour. Behavioural change was an outcome meas-
ured in five of the studies (LePage, 1999; Comaty et al.
2001; LePage et al. 2003; Hassell, 2009; Park & Lee,
2012). These studies included violent and aggressive be-
haviour in a physical or non-physical manner that could
harm or threaten other individuals or themselves, mainly
reported as ‘incidents’. All of the studies’ findings support
the efficacy of the token economy for reducing negative
behaviours and unit incidents related to these behaviours
on inpatient psychiatric units. It should be noted that one
study did not have a control, so improvements in behav-
iour and function cannot be attributed to the token economy
alone (Hassell, 2009). Further, one study and its results
provides data to support the positive long-term impact a
token economy can have on the safety and function of an
acute care unit (LePage et al. 2003).

Negative symptoms. The effect of the intervention on
negative symptoms was examined in two studies (Hopko
et al. 2003; Gholipour et al. 2012). The findings from both
studies support the efficacy of the token economy for
negative symptom reduction in an inpatient setting. How-
ever, the study by Hopko et al. (2003) only included
inpatients diagnosed with depression, and the study by
Gholipour et al. (2012) only included males with schizo-
phrenia. See Table 1 for a summary of the outcomes,
results, and the statistical data from each study.

Discussion

This systematic review supports the efficacy of a token
economy for reducing negative behaviours in adults with
mental health illness in an inpatient psychiatry setting. All
five of the studies analysing behavioural change showed
statistical significance in the reduction of negative behav-
iours. While the literature reviewed on the efficacy of the
economy for reducing negative symptoms supported the
use of the intervention, the number of studies was limited,
providing insufficient evidence to support its use. Further,
the symptom reduction studies only targeted one diagno-
sis, not representative of most psychiatric inpatient units.
The research on symptom reduction alone is scarce, and
this outcome measure needs to be explored further. All of
the studies looked at the effects within an inpatient setting,
and the regime of hospital units is important to con-
sider. This includes structure, schedules, expectations and
staff. None of the studies were able to look at the direct
effects on behaviour or symptoms outside of an inpatient
setting, so this is an important caution if considering use of

123© NAPICU 2016

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w

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d
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g
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xp
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le

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2
.

p
ro

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m

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&

P
D

th
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py
a

s
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tio

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e
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tio

n
n
=

1
7

n
=

1
5

in
t
h
e
P

IP
. S

M
I
n
=

1
0
1

n
=

5
9

6
.

n
=

5
5

3
.

C
o

m
p

le
tio

n
o

f
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o
n

tr
o

l n
=

1
5

.
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u
a

l d
ia

g
n

o
si

s
D

D
p
ro

g
ra

m
m

e
&

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D

n
=

4
6
.

n
=

1
N

o
n
-c

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m

p
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n

o
f
th

e
p

ro
g
ra

m
m

e
n
=

1
7
.

C
o
n
tin

u
e
d

124 © NAPICU 2016

GLOWACKI ET AL.

Ta
b

le
1

.
C

o
n

tin
u

e
d C
o

m
a
ty

e
t

a
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a
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125© NAPICU 2016

A TOKEN ECONOMY

R
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p
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t)

G
ro

u
p
d

iff
e
re

n
ce

s:
d
is

ch
a
rg

e
:

la
rg

e
(

d
=

0
.7

3
).

To
ta

l i
n
ju

ri
e
s:

3
3
%

p
<

0
.0

0
1
.

5
1
.7

0
±

8
.5

4
.

ch
a
n
g
e
(

p
<

0
.0

5
).

D
D

g
ro

u
p
a

t
a
d
m

is
si

o
n
:

M
e
a
n
a

n
d
s

ta
n
d
a
rd

3
5
.3

5
±

8
.6

5
a

n
d
a

t
d
e
vi

a
tio

n
n

o
t g

iv
e
n
.

d
is

ch
a
rg

e
:

5
4
.2

2
±

7
.0

8
.

C
o

n
c
lu

s
io

n
s

T
h
e
f
in

d
in

g
s

su
p
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rt

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h
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f
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in

g
s

su
p
p
o
rt

T
h
e
f
in

d
in

g
s

sh
o
w

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h
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f
in

d
in

g
s

su
p
p
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rt

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h
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f
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in

g
s

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p
p
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rt

T
h
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f
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s

su
p
p
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rt

T
h
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f
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in

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&
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c
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ti

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th

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l s

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fo

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im

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ro

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m

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p
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a

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d

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p

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d

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ci

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tiv
e

so
ci

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l b

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h

a
vi

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b

u
t

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d

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ct

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in

p
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tie
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d
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ci

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ta

l n
e
g
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tiv

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d
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ci

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in

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ri

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s

a
n
d

re
d
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ci

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g
a

g
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re

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vi

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rs

s
h
o
rt

-t
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rm

.
sy

m
p
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m
s

sh
o
rt

-t
e
rm

.
th

is
c

a
n
n
o
t
b
e

d
ia

g
n
o
se

d
w

ith
in

ci
d
e
n
ts

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n
a

n
a

cu
te

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g
a
tiv

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b

e
h
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vi

o
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rs

b
e
h
a
vi

o
u
rs

. T
h
e

It
a

ls
o
s

h
o
w

s
th

e
T

h
e
p

o
p
u
la

tio
n
w

a
s

a
tt
ri

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te

d
t
o
t
h
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t
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ke

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d
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p
re

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n
,
b
u
t
it

u
n
it

a
n
d
f
o
r

re
d
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ci

n
g

re
la

te
d
t
o
v

io
le

n
ce

o
n

p
o
p
u
la

tio
n
w

a
s

lim
ite

d
b
e
n
e
fit

s
fo

r
2
d

iff
e
re

n
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lim
ite

d
t
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m

a
le

s
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ith
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co

n
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m

y
a
lo

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ce

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n
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t
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g

e
n
e
ra

lis
e
d

p
a
tie

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t/
st

a
ff
in

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ry

.
a
n
in

p
a
tie

n
t
a
cu

te
to

m
a
le

s
in

a
n
in

–
cl

in
ic

a
l p

o
p
u
la

tio
n
s.

sc
h
iz

o
p
h
re

n
ia

.
th

e
re

w
a
s

n
o
c

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n
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l

d
u
e
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o
s

m
a
ll

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m

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le

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sy

ch
ia

tr
ic

u
n
it.

p
a
tie

n
t
p
sy

ch
ia

tr
y

g
ro

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p
t
o
c

o
m

p
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re

.
si

ze
. T

h
e
p

o
p
u
la

tio
n

se
tt
in

g
w

ith
a

h
is

to
ry

w
a
s

lim
ite

d
t
o

o
f
a
g
g
re

ss
iv

e
in

d
iv

id
u

a
ls

w
ith

M
D

D
.

b
e

h
a
vi

o
u

r.

A
D

L
: a

ct
iv

iti
e
s

o
f d

a
ily

li
vi

n
g
; B

A
T

D
: b

e
h
a
vi

o
u
ra

l a
ct

iv
a
tio

n
tr

e
a
tm

e
n
t f

o
r d

e
p
re

ss
io

n
; B

D
: b

e
h
a
vi

o
u
ra

l d
is

o
rd

e
r;

D
D

: d
u
a
l d

ia
g
n
o
si

s
M

D
D

: m
a
jo

r d
e
p
re

ss
iv

e
d

is
o
rd

e
r;

P
D

: p
sy

ch
ia

tr
ic

d
is

o
rd

e
r;

P
IP

: p
o
in

ts
in

ce
n
tiv

e
p

ro
g
ra

m
m

e
; S

M
I:

se
ri

o
u
s

m
e
n
ta

l i
lln

e
ss

; S
P

: s
u
p
p
o
rt

iv
e
p

sy
ch

o
th

e
ra

p
y;

S
T

T
E

: s
h
o
rt

-t
e
rm

t
o
ke

n
e

co
n
o
m

y