Topic: somatic symptom disorder this is a two part job, this is part one. i need 2 or 3 Scholarly Research Article that is related to this topic.

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

A TOKEN ECONOMY Ta

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C o n tin

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124 © NAPICU 2016

GLOWACKI ET AL.

Ta b

le 1

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o n

tin u

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m a ty

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