Psychological flexibility-based interventions versus first-line psychosocial interventions for substance use disorders: Systematic review and meta-analyses of randomized controlled trials

Journal of Contextual Behavioral Science 13 (2019) 109–120

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Psychological flexibility-based interventions versus first-line psychosocial interventions for substance use disorders: Systematic review and metaanalyses of randomized controlled trials

T

Toshitaka Iia,∗, Hirofumi Satoa, Norio Watanabeb, Masaki Kondoa, Akihiko Masudac, Steven C. Hayesd, Tatsuo Akechia a

Department of Psychiatry, Nagoya City University Graduate School of Medical Sciences, 1 Azanakawasumi Mizuho-cho Mizuho-ku, Nagoya, 467-8601, Japan Department of Health Promotion and Human Behavior, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan Department of Psychology, University of Hawaii at Manoa, 2530 Dole Street Sakamaki C 400 Honolulu, Hawaii, 96822-2294, USA d Department of Psychology, University of Nevada, Reno. 1664 N. Virginia Street Reno, Nevada, 89557-0296, USA b c

ARTICLE INFO

ABSTRACT

Keywords: Psychological flexibility Substance use disorders Acceptance and commitment therapy Third-wave cognitive behavioral therapy Meta-analysis

The third-wave cognitive behavioral therapies (CBTs), such as acceptance and commitment therapy (ACT) and dialectical behavior therapy (DBT), have been shown to be effective treatments for individuals with substance use disorders (SUD). Given their conceptual and methodological heterogeneity, however, it is difficult to categorize these third-wave CBTs and examine their clinical outcomes. One proposed method is to identify and categorize treatments based on their intended mechanisms of change, and then systematically examine their clinical effects. Psychological flexibility is theorized to be a potential process of change in many third-wave CBTs. In this study, we first identified third-wave CBTs that deliberately target psychological flexibility and categorized them as psychological flexibility–based interventions (PF interventions). PF interventions included ACT, DBT, mindfulness-oriented recovery enhancement, and distress tolerance therapy. We then conducted a metaanalysis of randomized controlled trials (RCT) that compared PF interventions to first-line psychosocial interventions. From a total of 2781 citations, our search identified 10 RCTs including a total of 658 participants. Compared to the first-line psychosocial interventions recommended for SUD treatment (e.g., brief motivational interventions, 12-step groups, and the like), PF interventions demonstrated a higher rate of substance discontinuation (33.6% vs 24.8%). There was no significant difference in dropout rate between the two groups of interventions. Third-wave CBT methods that target psychological flexibility are promising interventions for SUDs. Meta-analyses of this kind are a useful first step in taking a process-based therapy approach to the current evidence on psychosocial interventions.

Substance use disorders (SUD) have been defined as a cluster of psychiatric symptoms that indicate the continual use of a substance (or substances), despite significant substance-related issues, such as dependence and substance abuse (American Psychiatric Association, 2013). According to the United Nations Office on Drugs and Crime (2018), 275 million people worldwide (roughly 5.6% of the global population aged 15–64 years) used illicit drugs at least once during the past year, and about 31 million of those individuals suffer from SUDs. A range of psychosocial and pharmacological approaches are used as treatments for SUDs (World Health Organization, 2009). In part because of the scope of the SUD problem, however, it is difficult for systems of care to provide formal psychosocial interventions to all.

Formal psychosocial interventions are defined by the National Institute for Health and Care Excellence (NICE, 2012a,) as enhanced levels of intervention that require specific competencies to be delivered, supported by training and supervision. Examples of formal psychosocial interventions mentioned in the NICE guidelines include contingency management, behavioral couples therapy, community reinforcement, social behavior network therapy, psychodynamic therapy, and cognitive behavioral therapy. In an attempt to keep up with demand there is a tradition in SUD services of delivering low cost services by peers or para-professionals, or in very brief form. NICE guidelines recognize this reality, distinguishing formal psychological interventions in this area from what they term “first line psychosocial intervention.” These are

Corresponding author. E-mail addresses: [email protected] (T. Ii), [email protected] (H. Sato), [email protected] (N. Watanabe), [email protected] (M. Kondo), [email protected] (A. Masuda), [email protected] (S.C. Hayes), [email protected] (T. Akechi). ∗

https://doi.org/10.1016/j.jcbs.2019.07.003 Received 2 November 2018; Received in revised form 16 April 2019; Accepted 30 July 2019 2212-1447/ © 2019 Association for Contextual Behavioral Science. Published by Elsevier Inc. All rights reserved.

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defined as brief treatments or self-help focused on improving motivation, participation, likelihood of recovery, and prevention of relapse. Examples mentioned are brief motivational interventions, goal-setting, relapse prevention, psycho-education, contingency-management, problem-solving, and self-help groups such as 12-step (NICE, 2007; 2012a,). It helps understand the distinction to note that “contingency management” is on both lists since it can be a formal psychosocial intervention when training and supervision is required to design and implement a contingency program for a particular individual, or it can be a first line intervention when, for example, incentives are routinely used to reward clean urinalyses among program participants. Meta-analyses of traditional CBT and other formal psychosocial interventions have shown that they provide gains in terms of substance discontinuation for treatment of SUDs. For example, Dutra et al. (2008) found that traditional CBT as compared to waitlists or to treatment as usual yielded statistically significant but small increases in substance discontinuation (Cohen's d = 0.28) There is room for substantial improvement, however. When used alone, about one-third of those successfully treated with formal psychosocial interventions relapse within a year if they are suffering from SUD comorbid with other psychological disorders, and within condition effect sizes are only small to moderate (Blonigen, Finney, Wilbourne, & Moos, 2015; Dutra et al., 2008; Xie, McHugo, Fox, & Drake, 2005). Differential processes of change for CBT, or other formal psychosocial interventions for dependence, have not yet been established (Blonigen et al., 2015). Thus, it is potentially both practically and theoretically useful to expand the range of evidencebased formal psychosocial interventions for SUDs. Recently there has been a rise in interest in mindfulness- and acceptance-based psychosocial interventions as SUD treatments (Hayes & Levin, 2012). Largely studied as part of the “third wave” of behavioral and cognitive therapy (Hayes, 2004), examples of these treatment modalities include acceptance and commitment therapy (ACT; Hayes, Strosahl, & Wilson, 2012), dialectical behavior therapy (DBT; Linehan et al., 1999), mindfulness-based relapse prevention (MBRP; Kabat-Zinn, 1990), and mindfulness-based cognitive therapy (MBCT; Segal, Williams, & Teasdale, 2013). There is some preliminary indication that such methods may be useful as SUD treatments (Grant, Colaiaco, Motala et al., 2017; Lee, An, Levin, & Twohig, 2015; Smout et al., 2010; Stotts & Northrup, 2015). A challenge for the literature, however, is to categorize treatments beyond their theoretical lineage or their apparent formal similarity. As evidence-based therapies move toward more transdiagnostic models, this challenge will only increase. It has recently been argued that a better way to organize and synthesize current treatment evidence is to classify treatments based on their intended processes of change, and then to examine outcomes (Hayes & Hofmann, 2017; Stotts & Northrup, 2015). CBT, broadly defined, seems especially well prepared to take this step (Hayes, Luoma, Bond, Masuda, & Lillis, 2006) given its clarity about purported mechanisms of change and the diversity of approaches now covered by the various “waves” of CBT. The present study represents an initial attempt to follow that approach. Psychological flexibility is a transdiagnostic process of change (Hayes & Levin, 2012). Originally introduced within ACT, psychological flexibility is an adaptive and multifaceted set of six behavioral processes (reviewed later) that impact the ability to be open and present, and to focus on adaptive behavioral patterns (Hayes et al., 2012). Theoretically, improvement of psychological flexibility is thought to foster reductions in substance use due to a greater ability to deal with aversive experiences, without escape or avoidance, and to stay motivated to change. Psychological flexibility consists of six core processes: acceptance, cognitive defusion, valuing, behavioral commitment, being present, and self-as-context (Hayes et al., 2006; Hayes, Villatte, Levin, & Hildebrandt, 2011). Acceptance (i.e., psychological openness to difficult internal events) and cognitive defusion (i.e., noticing thoughts with a sense of distance in order to reduce their dominance over action) are

features of the acceptance-and-mindfulness process. Valuing (i.e., acknowledging qualities of being or doing that are deeply important to the individual) and behavioral commitment (i.e., concern over establishing larger patterns and habits of values-directed actions) are features of the commitment-and-behavioral change process. Being present (i.e., being able to attend to what is occurring at the moment, flexibly, fluidly, and voluntarily) and self-as-context (i.e., the experience of self as a space where personal experiences unfold) are theorized to be shared by the acceptance-and-mindfulness and commitment-and-behavioral-change processes. Psychological flexibility is arguably a targeted mechanism of change not only in ACT, but in other types of CBT as well (Hayes & Levin, 2012). For example, DBT targets mindfulness and behavioral change processes (Linehan et al., 1999). Similarly, distress tolerance treatment (DT; Stein et al., 2015) for clients with opioid dependency includes flexibility skills drawn from ACT. Throughout evidence-based therapy, an early step in evaluating formal psychosocial interventions is generally to see if they produce better outcomes than interventions not requiring specific competencies supported by special training and supervision in order to be delivered. This scientifically conservative approach is reflected in the tendency of early meta-analyses of intervention methods to compare technologies to wait-lists or to treatment as usual. An alternative early step exists in SUD because of the wide-spread use of first-line interventions such as 12-step or brief motivational interventions. The logic of an early comparison between the two is reflected in the NICE guideline recommendations that formal psychosocial treatments be used only if first line treatments fail or if clinical judgment suggests that more intensive treatment is initially necessary (NICE, 2012a,). This conservative recommendation makes perfect sense in terms of resource allocation by overloaded systems of care. Given its conceptual and clinical relevance, psychological flexibility was used in the present study as a criterion for identifying and classifying third-wave CBTs to systematically examine their effects on SUDs. Psychological flexibility-based interventions (PF interventions) were then compared to first line psychosocial interventions as described by NICE. That approach was taken for two reasons. First, there are sufficient data to make such a comparison. Well-crafted and appropriately powered head-to-head comparisons of formal psychosocial interventions for SUDs are necessarily large, expensive, and rare. Secondly, PF interventions are still in their nascent phase of development as treatments for SUDs, as is reflected by the fact that none are yet specifically yet mentioned in the NICE guidelines. Thus, for both scientific and systems resource reasons, a conservative early step is to determine if formal PF interventions are superior to first-line psychosocial treatments. If so, evidence-based practitioners would have available a larger range of process-based alternatives for deployment when first-line treatments fail, or if case severity warrants. 1. Methods This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. It was registered in the PROSPERO International Prospective Register of Systematic Reviews (ID=CRD42016041837; National Institute for Health Research, 2016), and the search strategy, inclusion criteria, data extraction, subgroups, and sensitivity analysis adhered to the registered protocol. 1.1. Eligibility criteria To be eligible, a given randomized clinical trial (RCT) had to investigate the efficacy of a PF intervention compared with a first-line psychosocial intervention for SUDs in any study setting (e.g., primary care, inpatient, community-based, secondary, or specialist settings). SUD diagnosis had to be based on a structured assessment for diagnostic 110

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criteria of the DSM-IV (American Psychiatric Association, 1994), DSM-5 (American Psychiatric Association, 2013), ICD-10 (World Health Organization, 1992), or other formal assessment criteria. Substance in the present study referred to alcohol, amphetamines, cannabis, cocaine, designer drugs, heroine, methamphetamines, other narcotics, and street drugs, as well as prescription drugs such as benzodiazepines. Participants comorbid for physical or common mental disorders were eligible for inclusion, as were all ages and genders. Interventions were defined based on their flexibility targets. In accordance with a prior meta-analysis of ACT (Öst, 2008), any given third-wave CBT intervention was judged to be a PF intervention if its intervention protocol targeted at least 3 out of the 6 core flexibility processes; and, since psychological flexibility is defined as the ability to be open and present and to focus on adaptive behavioral patterns, we added the following two criteria to this review: 1) at least one process was cognitive defusion or acceptance; and 2) at least one process was values or commitment. Any given mindfulness-based intervention (MBI) was judged to be a PF intervention if it included not only the mindfulness processes, but also valuing or behavioral commitment. For example, as described below, mindfulness-oriented recovery enhancement (MORE) in Garland et al. (2014) was judged to be a PF intervention, as its protocol included both acceptance-based techniques to reverse anhedonia, and techniques intended to strengthen the motivation to engage in valued activities. In contrast, the mindfulness-based relapse prevention (MBRP) protocol described in Bowen et al. (2014) was not judged to be a PF intervention as it did not include a component of valuing or behavioral commitment as defined by the Psychological Flexibility Model (see Hayes et al., 2011 for how the Psychological Flexibility Model is applied to a range of third-wave and mindfulness-based CBTs). For this same reason, mindfulness-based stress reduction (MBSR; Kabat-Zinn, 1990) and mindfulness-based cognitive therapy (MBCT; Segal et al., 2013) were excluded from the present review. Following the NICE guidelines, first-line psychosocial interventions for SUD were defined to include brief motivational interventions, goal setting, relapse prevention, psycho-education, and problem-solving. Psychosocial advice drawn from evidence-based formal psychological interventions (e.g., for comorbid psychiatric disorders) was considered psycho-education and was included in the first-line intervention category. Self-help programs such as Narcotics Anonymous and Cocaine Anonymous were also included. In all cases these were based on 12-step principles. Formal psychosocial interventions as defined and listed earlier were excluded from first-line interventions. Because contingency management was included on both lists, it was excluded to prevent categorical errors. Following the PRISMA guidelines, substance discontinuation (defined as less than one use per week) at the end of treatment and up to longest follow up after starting the intervention (usually six months after starting the interventions) was examined as the primary outcome. Secondary outcomes included dropout rates (defined as dropout from the intervention); improvements in measures of substance dependence, depressive symptoms, anxiety symptoms, psychological flexibility as measured by the Acceptance and Action Questionnaire or related instruments (AAQ-Ⅱ; Bond et al., 2011), and quality of life. The same time frame was used to examine the effects of the intervention on these secondary outcomes as for the primary outcome.

methadone; MDMA; morphine; ecstasy; methamphetamine; narcotics; opioid; opiate; opium. In addition, the authors conducted a hand search of the web page of the Association for Contextual Behavioral Science list randomized trials (https://contextualscience.org/state_of_the_act_ evidence). 1.3. Data collection An eligibility assessment was performed independently by two of the authors (TI and HS), both of whom had ACT training, with a special focus on the Psychological Flexibility Model, and at least one year of clinical and research experience as an ACT therapist. First, the titles and abstracts were screened, based on the inclusion criteria (e.g., RCTs, interventions based on psychological flexibility, and patients with SUDs). The studies that met the criteria were then added to the preliminary list. Any disagreements between the reviewers were resolved by discussion. To summarize the study selections, the PRISMA flow diagram was employed (Higgins et al., 2011). As described below in detail, eligibility assessment of peer-reviewed articles showed that four methods in 10 RCTs met these criteria: ACT, DBT, and DT, and MORE. As described above, some notable third-wave CBTs and MBIs, such as MBRP (Bowen et al., 2014), MBSR (Kabat-Zinn, 1990), MBCT (Segal et al., 2013), and behavioral activation (Martell, Dimidjian, & HermanDunn, 2013), were excluded because required psychological flexibility processes were left untargeted (c.f., Hayes et al., 2011). To examine these 10 RCTs further, the present review used the Cochrane Review data extraction template. Authors TI and HS independently extracted the following data: the details of the trial methods (e.g., country, design, trial duration, and ethical approval); the participants’ characteristics (e.g., population description, setting, age, sex, and race); the types of interventions (e.g., the name of the intervention, its details, length, mode of delivery, and any co-intervention); and the types of outcomes (e.g., outcome name, time points, the person measuring the outcome, and validation). Each independently determined sequence generation (selection bias due to inadequate generation of a randomized sequence) and allocation concealment (selection bias due to inadequate concealment of allocations prior to assignment), blinding of the participants and providers (performance bias), blinding of the outcome assessor (detection bias), incomplete outcome data (attrition bias), selective outcome reporting (reporting bias), and other sources of bias were also extracted. The present review judged an RCT to be at high risk of attrition bias if the dropout rate of the intervention in the study was more than 20%. The risk of bias was judged in terms of low, high, or unclear risk, based on the criteria in the Cochrane Handbook for Systematic Reviews of Interventions (DerSimonian & Laird, 1986). 1.4. Coding strategy The present review also included factors relevant to treatment outcome studies, and they were coded based on Wampold et al. (2011). Coded records were extracted independently by the two authors (TI and HS), and disagreements were then resolved by discussion. Relevant factors coded included: a) number of therapists; b) dose hours; c) training; d) supervision; e) adherence; and f) researcher's allegiance to the treatment. Regarding researcher's alliance, the present study used a six-point coding system: 5 = the PF intervention was developed by the author(s), and they supervised or trained the therapists; 4 = the PF intervention was developed by the authors, but they did not train or supervise the therapists; 3 = the PF intervention was advocated by one of the authors and they also supervised/trained the therapists; 2 = the PF intervention was advocated by the authors but they did not train or supervise therapists; 1 = the PF intervention was more fully explained in the introduction and/or method section than the alternative; 0 = there was no apparent advocacy over one treatment than the other. In the present review, this coding system was also used to judge

1.2. Search strategy Relevant studies were identified by searching electronic databases and scanning reference lists, including MEDLINE, PsycINFO, Scopus, CINAHL, and the Cochrane Library (see Appendix A for actual key words used for the search). The last search was performed on July 12, 2016. The following substances were used in the electronic database search: alcohol; amphetamines; drug; polydrug; substance; cannabis; cocaine; hash oil; hashish; heroin; LSD; marihuana; marijuana; 111

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whether a study had better trained or more experienced therapists for one treatment over another, while there was no apparent advocacy for either treatment modality. Furthermore, the present review also coded first-line psychosocial interventions using a coding system informed by Wampold et al. (2011), called treatment-as-usual proximity to psychotherapeutic intervention (TAU-PPI; see Table 1). A study was rated as 1 if all participants in the first-line psychosocial intervention condition received a psychotherapeutic treatment delivered by a mental health professional, doing what they usually do with no prescription for psychotherapeutic care, but where the PF therapist received additional training and supervision or the PF intervention patients received a greater dose of treatment. A study was rated as 2 if patients in the first-line psychosocial intervention condition received a specified first-line psychosocial intervention, which was tracked by the researchers in order to ensure that the patients in fact received that treatment, but it was still unclear exactly what type of first-line psychosocial intervention was used or whether all participants received the same intervention. In studies coded as 3, therapists were crossed, meaning that PF intervention therapists also provided patients the specified first-line psychosocial intervention condition (e.g., drug counseling), which was tracked by the researchers. The code of 4 was given to a study if the first-line psychosocial services were suggested or mentioned by the researchers to those randomized to the comparison condition, but the services were not tracked, reported, or discussed in the study.

meet the criteria for PF interventions, 6 because their comparison conditions did not meet the criteria for first-line psychosocial interventions, 5 due to a lack of randomization, 2 that were re-analyses, and one due to lack of any clinical outcome. After reviewing the bibliographies of the seven studies that remained, three additional studies were identified that met the inclusion criteria. As a result, 10 RCTs formed the focus of the present analysis. Of those, seven RCTs reported the primary outcome, and they were included in the quantitative synthesis. 2.1. Study characteristics Of these 10 RCTs, four studies examined ACT (Hayes et al., 2004; Luoma, Kohlenberg, Hayes, & Fletcher, 2012; Petersen & Zettle, 2009; Stotts et al., 2012), three studies investigated DBT (Courbasson, Nishikawa, & Dixon., 2012; Linehan et al., 1999, 2002), two examined MORE (Garland et al., 2014; Garland, Roberts-Lewis, Tronnier, Graves, & Kelley, 2016), and one studied DT (Stein et al., 2015). The four ACT studies were judged to cover all of the six core processes of psychological flexibility; the DBT, DT, and MORE studies were judged to use four of the six core processes (i.e., acceptance, being in the present moment, valuing, and behavioral commitment). A total of 655 participants were included in the 10 RCTs, with treatments that ranged from one month to twelve months in duration after the post-randomization (see Table 1). Nine studies were conducted in the United States, and one in Canada (Courbasson, Nishikawa, & Dixon, 2012). All studies were published in English. Ethical approval was described in six studies (Courbasson et al., 2012; Garland et al., 2014, 2016; Linehan et al., 2002; Petersen & Zettle, 2009; Stotts et al., 2012). The participants mainly consisted of adults diagnosed with SUDs using the DSM-Ⅳ criteria. Those with cognitive impairments, psychotic or bipolar disorders, and suicidality were excluded from most of the studies. For recruitment, three studies used advertisements (Luoma et al., 2012; Stein et al., 2015; Stotts et al., 2012), while the others recruited patients from affiliated clinics or hospitals. The average participant age ranged from 30.4 to 48.3 years, with a total range of 25–60 years of age. Three studies were limited to females (Courbasson et al., 2012; Linehan et al., 1999, 2002), and one study included males only (Garland et al., 2016). The participants in the remaining four studies were of mixed gender, with between 32% and 72% being male. One study (Courbasson et al., 2012) had only White participants, while other studies included between 13% and 68% nonWhites. In three of the studies (Garland et al., 2014; Linehan et al., 1999, 2002), the majority of the participants had a college education. Four studies (Courbasson et al., 2012; Luoma et al., 2012; Petersen & Zettle, 2009; Stotts et al., 2012) reported that the majority of their participants were high school graduates. Regarding other patient characteristics, 14.4–52.0% of the participants worked full-time, and 46.4–72.0% were either single or living alone. With the exception of one study (Luoma et al., 2012), treatment providers were master's level or higher clinicians with some experience in the assigned intervention. The number of therapists per treatment condition was between 1 and 4, and only one study (Linehan et al., 2002) had the PF intervention with two more therapists than the firstline psychosocial intervention condition. Dose of hours ranged from a minimum of 3.1 h (Petersen et al., 2009) to a maximum of 192 h (Linehan et al., 1999). All the studies used intervention manuals; two studies included training workshops for study therapists (Hayes et al., 2004; Stotts et al., 2012); seven studies described the supervision processes for the PF intervention, and five of them also described those processes for the first-line psychosocial intervention (Courbasson et al., 2012; Garland et al., 2014; Linehan et al., 1999, 2002; Stotts et al., 2012), while two others did not (Garland et al., 2016; Petersen & Zettle, 2009; Stotts et al., 2012). Only one study (Luoma et al., 2012) reported the amount of supervision (i.e., 30 h). Three studies (Hayes et al., 2004;

1.5. Statistical analysis RevMan 5 software was used for this meta-analysis. A random-effects model (DerSimonian & Laird, 1986) and the Mantel-Haenszel method were used to pool the results of the binary variables, and continuous data was calculated by standard mean differences (Mantel & Haenszel, 1959). I-squared statistic (Higgins & Thompson, 2002) was computed to detect any possible statistical heterogeneity among the trial outcomes. More specifically, p < 0.05 for chi squared test and Isquares of 50% or greater were considered being indicative of heterogeneity. An intention-to-treat approach was used to calculate dropout rates for interventions. Descriptive terms for effect size estimates were based on Cohen (1988). Sensitivity and subgroup analyses were pre-specified for judging whether there was a low risk of selection bias for the sequence generation or allocation concealment. In particular, subgroup analyses were used to determine the following: 1) the severity of dependence diagnosed by DSM, ICD, or other standardized criteria; 2) the type of intervention, such as face-to-face, group, computer-based, or self-help; 3) the location of the intervention, such as a clinic, hospital, community health center, or prison; 4) whether the intervention targeted all six, or fewer, components of psychological flexibility; and 5) whether a PF intervention was delivered as a standalone or delivered as an adjunct to other SUD treatment. Sensitivity considerations resulted in limiting the trials to those with a low risk of selection bias in allocation concealment and excluding two studies where imputation methods were used. 2. Results The search of the MEDLINE, Cochrane, PsycINFO, Scopus, and CINAHL databases provided a total of 2781 citations (see Fig. 1). After duplicates were removed and one citation, which was identified through a hand search, was added, 2371 citations remained. Of these, 2279 studies were excluded as they did not meet initial inclusion criteria (i.e., setting, diagnostic criteria, substances, and intervention). Disagreement between reviewers occurred 36 times across 2371 ratings (Cohen's kappa = .927). After examining the full texts of the remaining 92 citations, 85 were excluded because they did not meet the specified inclusion criteria: 43 because study participants did not meet the diagnostic criteria for SUDs, 28 because the target intervention did not 112

Outpatients Outpatients

DBT

DBT

Stotts et al. (2012)

Linehan et al. (1999)

Linehan et al. (2002)

MORE

Garland et al. (2016)

113

32 ind + 24 Gro 32 ind + 24 Gro 6 Gro + TAU TAU (8 ind + Gro) 3.1 ind 4.3 ind 24 ind 24 ind 64 ind + 128 Gro N/A 50 ind + 125 Gro 50 ind + 100 Gro 50 ind + 100 Gro 75 ind 16 ind 16 ind 20 Gro 20 Gro 7 ind 3.5 ind

Hayes et al. (2004)

Workshop Workshop Pilot project N/A N/A N/A Workshop 2d N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Train hours PF/ First-line

Outpatients

Sup Sup Sup Sup Sup Sup Sup Sup N/A N/A

Sup Sup Sup 30nr Sup Team + Sup Team + Sup Phone sup N/A Team meeting

Supervision: Sup hours PF/First-line

Opioid

Variety

Opioid

Variety

Variety

Opioid

Alcohol

Variety

Variety

Substance

Tape Tape Tape N/A Tape N/A Tape Tape N/A N/A N/A N/A N/A N/A Video Video N/A N/A Tape Tape

Adhere checks

Psychiatric Disorders None

Chronic pain

ED

BPD

BPD

None

Depression

None

None

Comorbidity

5

5

5

2

4

5

3

3

3

4

Allegiance

49

116

115

22

23

27

56

28

3

86

Number

4

4

2

4

4

4

2

4

4

1

TAU PPI

DT

Group MORE

Individual + Group DBT Individual + Group DBT Individual + Group DBT Group MORE

Individual ACT

Group ACT + TAU Individual ACT

Individual ACT

Intervention

3 months (7 weeks)

10 weeks (10 weeks)

16 months (16 months) 16 months (12 months) 12 months (12months) 5 months (8 weeks)

5 months (5 months)

1 months (1-months)

4 months (28-days)

8 months (16-weeks)

Post-randomization Treatment periods (Length of Treatment)

buprenorphine

methadone

Methadone

Methadone

Pharmacological treatment

1.34 [0.59–3.02]

N/A

1.70 [0.92–3.14]

N/A

0.82 [0.42–1.60]

2.00 [0.39–10.16]

1.91 [0.76–4.77]

N/A

1.13 [0.72–1.78]

1.68 [0.60–4.72]

Discontinuation Risk Ratio (RR)

TAU (psychoeducation) Health Education

TAU (MI + CBT for ED and SUD) Support group

CVT + TSF

TAU condition

Methadone maintenance + ITSF TAU (Group + ITSF) TAU (ITSF + alleviationdepression) Drug Counseling

First-line Comparison

Z = 0.70

N/A

Z = 1.68

N/A

Z = 0.59

Z = 0.84

Z = 1.38

N/A

Z = 0.53

Z = 0.98

Effect size

2: master 2: master 5: master 2:Theapist 3: > master 2: master 3: > master 3: > master 1: master 1: master 1: master 1: master 2: > master 2: > master

3: > 4 years 3: > 4 years Author Counsellor

4: master 3: > 5 years

Therapists PF/ First-line

N/A = information not available. Note: ACT = acceptance and commitment therapy; BPD = borderline personality disorders; CBT = cognitive behavior therapy; controlling key threats = CVT = comprehensive validation therapy; DBT = dialectical behavioral therapy; DT = distress tolerance; ED = eating disorders; Gro = Group; ind = individual; ITSF = intensive twelve-step facilitation; MI = motivational interviewing; MM = methadone maintenance, MORE = mindfulness-oriented recovery enhancement; SUD = substance use disorder; TAU = treatment as usual; TAU condition = control for several key threats to internal validity; TSF = twelve-step facilitation.

Stein et al. (2015)

Garland et al. (2016)

Garland et al. (2014)

Courbasson et al. (2012)

Linehan et al. (2002)

Linehan et al. (1999)

Stotts et al. (2012)

Petersen et al. (2009)

Luoma et al. (2012)

Dose hours PF/ First-line

Study

Stein et al. (2015)

Outpatients

MORE

Garland et al. (2014) Homeless

Outpatients

Courbasson et al. (2012)

Outpatients

Inpatients

Petersen et al. (2009)

Clinic Inpatients

ACT

Hayes et al. (2004)

Setting

Luoma et al. (2012)

founder

Study

Table 1 Summary of the included studies.

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Fig. 1. PRISMA flow chart of study selection.

Stein et al., 2015; Stotts et al., 2012) audio-recorded both PF intervention and first-line psychosocial intervention. Two studies (Luoma et al., 2012; Petersen et al., 2009) audio-recoded PF intervention, but not their first-line psychosocial intervention. One study (Garland et al., 2014) video-recorded both interventions. As for pharmacological treatment, participants in three studies (Hayes et al., 2004; Linehan et al., 2002; Stotts et al., 2012) received methadone, and in one study (Stein et al., 2015), participants received buprenorphine.

reporting, and three used urine tests to examine the primary outcomes (i.e., substance discontinuation; Hayes et al., 2004; Stein et al., 2015; Stotts et al., 2012) – these were judged low for risk of blind assessment bias. For attrition bias, all studies were missing some outcome data and therefore were judged high in attrition bias. Regarding the reporting bias, the primary outcome end-point was described in the protocol for one study (Luoma et al., 2012), but this protocol did not describe all outcomes. We therefore judged all 10 studies to be unclear in selective reporting bias. Finally, since one ACT study, two DBT studies, and both the MORE and DT studies were conducted by the founders of the target interventions, there were potential biases favoring the PF conditions.

2.2. Risk of bias For the random sequence generation, six of the 10 studies were considered to have a low risk of selection bias, given that there was sufficient information about the details of the methods of random sequence generation (see Table 2). Regarding allocation concealment, five of the 10 studies were considered to have a low risk of selection bias, and five studies were found to have an unclear risk of bias due to a lack of information about the method of allocation concealment. Logically, it is not possible to “blind” participants for any psychosocial method so it was not surprising that none of the studies addressed the issue. Regardless, we decided to take the conservative route and list all as high risk of performance bias, in order to note that differences in expectations, believability, buy-in, and similar processes are inherent to psychosocial methods. Six studies used blind assessors for outcome

2.3. Effects of PF intervention For the primary outcome (i.e., substance discontinuation), relevant data were available from seven studies (see Fig. 2). In comparison with first-line psychosocial interventions, PF interventions showed a small, but significantly higher, rate of substance discontinuation (risk ratio: RR = 1.36; 95% confidence interval: CI = 1.04 to 1.79, p = 0.03), and there was no significant difference in the rate of substance discontinuation among those seven studies (chi-squared test = 4.24, p = 0.64, I2 = 0%). The dropout rate from PF interventions was 42.7%, compared with 41.4% for first-line psychosocial interventions (see Fig. 3), yielding a non-significant difference (10 studies: RR = 0.98; 114

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first-line psychosocial interventions (2 studies: The Acceptance and Action Questionnaire [Bond et al., 2011] used in [Petersen & Zettle, 2009]; Avoidance and Inflexibility-Substance Use Scale used in [Stotts et al., 2012]; SMD = −0.63; 95% CI = −1.48 to −0.21, p = 0.14, I2 = 55%). Only one study reported anxiety symptoms, with a nonsignificant difference (1 study: [Garland et al., 2016]; SMD = −0.10; 95% CI = −0.54 to 0.34, p = 0.66), and two reported quality of life, with a medium but non-significant difference (2 studies: Garland et al., 2014; Luoma et al., 2012; SMD = 0.58; 95% CI = −0.11 to 1.28, p = 0.10, I2 = 72).

Table 2 Estimated risk of bias across all included studies.

2.4. Sensitivity analyses Six of the 10 studies were judged to be at low risk of selection bias and four were deemed to be at low risk of allocation concealment. Three studies (Hayes et al., 2004; Luoma et al., 2012; Petersen & Zettle, 2009) were judged to be of high or unclear risk for both random sequence generation and allocation concealment. Of these three studies, two were included in the analysis of primary outcome. It is worth noting, however, that they had been excluded, the PF interventions would have shown somewhat larger effects for the primary outcome as compared with first-line psychosocial interventions (5 studies: RR = 1.47; 95% CI = 1.02 to 2.11, p = 0.04) and thus their inclusion cannot be the source of the documented differences. Additional sensitivity analyses were conducted for the studies requiring imputation. For the primary outcome, one study (Garland et al., 2014) reported how many participants met diagnostic criteria for SUDs, rather than substance discontinuation per se, and therefore these values were imputed. When this study was excluded, PF interventions were not found to be significantly more effective than first-line psychosocial interventions (6 studies: RR = 1.28; 95% CI = 0.94 to 1.74, p = 0.11). Two studies required some type of imputation method for the secondary outcomes. One study (Petersen & Zettle, 2009) did not report depression outcomes and baseline rates were therefore carried forward, creating an effect size of zero. A second study (Luoma et al., 2012) did not report the standard deviation value of their process measure of psychological flexibility, the Avoidance and Inflexibility Scale. That value was imputed from a similar study (Salkovskis, 2002). Nevertheless, excluding both studies, improvements in depressive symptoms (4 studies: SMD = −0.39; 95% CI = −0.71 to −0.07, p = 0.02) and psychological flexibility (1 study: SMD = −0.23; 95% CI = −0.97 to 0.50, p = 0.53) were maintained. 2.5. Additional analyses within PF interventions A series of planned comparisons were made, dividing studies into subgroups. It should be noted that, when dividing a small set of studies into subgroups, Type II errors can increase and, therefore, the overall pattern and effect size of these comparison are more important than statistical significance per se. In the present review, we identified three ACT studies and four non-ACT PF interventions that reported data on substance discontinuation. There was no significant subgroup difference (p = 0.89) between ACT and first-line psychosocial interventions (3 studies: RR = 1.34; 95% CI = 0.92 to 1.96, p = 0.13) nor between non-ACT PF interventions and the first-line psychosocial intervention group (4 studies: RR = 1.39; 95% CI = 0.94 to 2.06, p = 0.10) in substance discontinuation. There was no statistically significant heterogeneity in ACT interventions (chi-squared test = 1.29, p = 0.53, I2 = 0%), nor in non-ACT PF interventions (chi-squared test = 2.99, p = 0.39, I2 = 0%), nor between ACT and non-ACT PF interventions (chi-squared test = 0.02, I2 = 0%). The PF interventions and first-line psychosocial interventions did not differ (p = 0.83) in substance discontinuation when delivered in an individual format (5 studies: RR = 1.41; 95% CI = 0.93 to 2.13) or when delivered as a group (2 studies: Luoma et al., 2012; Garland et al.,

95% CI = 0.77 to 1.24, p = 0.86), and there was no statistical heterogeneity in the dropout rate among these 10 studies (chi-squared test = 14.09, p = 0.12, I2 = 36%). For other secondary outcomes, improvements in substance dependence measures showed a small and significant difference between PF interventions and first-line psychosocial interventions (4 studies; standard mean difference measures: SMD = −0.35; 95% CI = −0.71 to 0.00, p = 0.05, I2 = 23%), favoring the PF intervention. Depressive symptom measures also showed a small and significant difference between the two types of interventions, favoring the PF group (5 studies: SMD = −0.41; 95% CI = −0.68 to −0.14, p = 0.003, I2 = 16%; see Fig. 4). Two studies reported psychological flexibility and showed a medium but non-significant difference between PF interventions and 115

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Fig. 2. Forest plot: ACT or other PF interventions vs. first-line psychosocial interventions for substance discontinuation. Note: ACT - acceptance and commitment therapy, PF - psychological flexibility.

Fig. 3. Forest plot: ACT or other PF interventions vs. first-line psychosocial interventions for dropout rates. Note: ACT - acceptance and commitment therapy, PF Psychological flexibility.

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Fig. 4. Forest plot: ACT or other PF interventions vs. first-line psychosocial interventions for depressive symptoms. Note: PF - Psychological flexibility.

2014; RR = 1.33; 95% CI = 0.92 to 1.91). The PF intervention and first-line psychosocial intervention did not differ (p = 0.33) in an outpatient setting (6 studies: RR = 1.50; 95% CI = 1.06 to 2.11) nor in an inpatient setting (1 study: Luoma et al., 2012; RR = 1.13; 95% CI = 0.72 to 1.78). Additionally, no subgroup difference (p = 0.52) was found between PF interventions and first-line psychosocial interventions when PF interventions were delivered as a standalone therapy (5 studies: RR = 1.47; 95% CI = 1.02 to 2.11) or when delivered as an adjunct to first-line SUD treatment (2 studies: Hayes et al., 2004; Luoma et al., 2012; RR = 1.23; 95% CI = 0.81 to 1.86). PF interventions did not differ (p = 0.69) from first-line psychosocial interventions in substance discontinuation, whether the founder of the target PF intervention was a primary research investigator (5 studies: RR = 1.43; 95% CI = 0.99 to 2.07) or not (2 studies: Luoma et al., 2012; Stotts et al., 2012; RR = 1.28; 95% CI = 0.85 to 1.92). No subgroup difference (p = 0.42) was found in substance discontinuation for patients with severe SUDs (6 studies; RR = 1.28; 95% CI = 0.94 to 1.74) nor patients with mild SUDs (1 study: Garland et al., 2014; RR = 1.70; 95% CI = 0.94 to 3.14). Finally, there was no subgroup difference (p = 0.33) in outcome assessment blinding (6 studies: RR = 1.50; 95% CI = 1.06 to 2.11) or unblinding (1 study: Luoma et al., 2012; RR = 1.13; 95% CI = 0.72 to 1.78). Finally, the present review conducted post-hoc analyses to further investigate the relative effects of PF intervention. First, we examined whether differential effects of PF interventions depended on the quality of the comparison group (i.e., first line psychosocial intervention). To do so, we used the TAU-PPI coding system and dichotomized the studies into high quality first line psychosocial intervention group (TAUPPI = 1 or 2) and low quality first line psychosocial intervention group (TAU-PPI = 3 or 4). Of the 7 studies used for substance use discontinuation, the statistical difference between PF interventions and first-line psychosocial intervention were present in the two studies (i.e., Hayes et al., 2004; Stotts et al., 2012) that met the criteria of high quality first line psychosocial intervention (RR = 1.80; 95% CI = 0.90 to 3.57, p = 0.03) and the remaining five studies that were coded as low quality first line psychosocial interventions (RR = 1.27; 95% CI = 0.95 to 1.71, p = 0.03). Our results revealed that there was no statistically significant heterogeneity in the high quality first-line psychosocial intervention group (chi-squared test = 0.03, p = 0.85, I2 = 0%), nor in the low quality first-line psychosocial intervention group (chi-squared test = 3.08, p = 0.54, I2 = 0%). We also found there to be no statistically significant subgroup difference and heterogeneity between high quality first-line psychosocial interventions and low quality first-line psychosocial interventions (chi-squared test = 0.81, p = 0.37, I2 = 0%). Second, we found no significant subgroup difference (p = 0.69) in substance discontinuation when PF intervention were delivered by primary researchers with high allegiance (allegiance rating > 4) (5

studies: RR = 1.43; 95% CI = 0.99 to 2.07) as compared to with low allegiance (allegiance rating of 3 or lower) to the interventions (2 studies: Stotts et al., 2012; Luoma et al., 2012; RR = 1.91; 95% CI = 0.76 to 4.77). Third, there was no significant subgroup difference (p = 0.17) in substance discontinuation between PF interventions with a duration of 5-months or longer (5 studies; RR = 1.53; 95% CI = 1.05 to 2.23) and PF interventions with a duration of 4-months or shorter (2 studies; Stein et al., 2015; Luoma et al., 2012; RR = 1.18; 95% CI = 0.79 to 1.75). Lastly there was no subgroup difference (p = 0.95) between PF intervention combined with a pharmacological treatment (3 studies; Hayes et al., 2004; Linehan et al., 2002; Stotts et al., 2012; RR = 1.38; 95% CI = 0.84 to 2.27) and PF interventions without pharmacological treatment (4 studies; RR = 1.38; 95% CI = 0.98 to 1.88). 3. Discussion This “proof of concept” study is the first systematic review and meta-analysis of randomized controlled trials based on interventions that target psychological flexibility as a potential mechanism of change. Based on the a priori criteria we specified, four different PF interventions were used to treat SUDs: ACT (Hayes et al., 2004; Luoma et al., 2012; Petersen & Zettle, 2009; Stotts et al., 2012), DBT (Courbasson et al., 2012; Linehan et al., 1999, 2002), DT (Stein et al., 2015), and MORE (Garland et al., 2014, 2016). On the pre-specified primary outcome, PF interventions showed a small, but significantly higher, rate of substance discontinuation than first line psychosocial interventions (i.e., 34.0% for PF interventions vs. 24.8% for first line psychosocial interventions). That difference is equivalent to a number needed to treat of 11.4 (NNT; the number of PF intervention patients that would have to be treated in order to create one good outcome in comparison with the control treatment), and a Cohen's d of 0.20. There was no significant heterogeneity among PF interventions in substance discontinuation (I2 = 0%, p = 0.39). A series of post hoc analyses suggested that substance discontinuation outcomes favoring PF intervention were not moderated by the quality of comparison condition (i.e., treatment-as-usual proximity to psychotherapeutic intervention), extent of the investigator's allegiance to PF intervention, and the length of treatment. Only one study (Stotts et al., 2012) was low in allegiance to PF intervention and had a high quality first line psychosocial intervention (i.e., TAU-PPI = 1), however. Depression and substance dependence measures indicated a small effect size favoring PF interventions. Two ACT studies included process measures related to psychological flexibility, and our meta-analysis showed significant and medium effect sizes favoring ACT. These findings remained consistent even after excluding two studies that were judged to have biases favorable to the PF intervention. Prior to this analysis, a general meta-analysis of psychosocial 117

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interventions for SUDs (Dutra et al., 2008) found success rates in terms of substance discontinuation of 31.0%, with a dropout rate of 35.4%. Participants were similar to this review (participants were diagnosed as having SUDs, average age 34.9 years, with more than half single or unmarried), but, unlike the present analysis, controls included wait-list groups. An initial meta-analysis of ACT for SUDs was recently carried out by Lee et al. (2015) and found a small, significant effect size for ACT of 0.43 (95% CI: 0.25 to 0.61), compared with other therapeutic interventions, but that study included smoking cessation trials. The present review of PF interventions showed broadly similar effects when smoking cessation trials were removed from the analyses. What is unique about the present analysis is that a variety of thirdwave CBTs that target psychological flexibility were compared to first line interventions. We conclude that, even in SUDs that meet diagnostic criteria, interventions based on a psychological flexibility model appear to be effective when compared to first line forms of therapy. We also conducted a series of outcome analyses organized by subgroups (e.g., type of intervention, individual versus group). None of the group differences were significant in these subgroup analyses, in part due to small sample sizes, but nothing in the pattern of these subanalyses suggest that the overall findings are an artifact. The main strength of the present study was the inclusion criteria. We included third-wave interventions based on the targeted process of change (i.e., the coverage of two major facets of the psychological flexibility process; and 3 out of the overall 6 in total), not on the “brand name” of the therapy (e.g., “ACT”). In the context of evidence-based medicine, it is important to go beyond the “package” or “brand name” of interventions in classification and evaluation. A brand-name approach linked to techniques is not necessarily progressive in scientifically developing, refining, and synthesizing the important features of intervention (Hayes & Hofmann, 2018), and particularly in generating and using information about the core processes of change to fit treatment elements to the individual. In the context of the large number of known change processes, it is necessary to have models that simplify them and organize them into a set, however, and this requires names and labels. Psychological flexibility originated in ACT, but developers of other treatment approaches might not wish to use “the ACT model” as a term (Hayes et al., 2012) – use of the term, Psychological Flexibility Model avoids that difficulty. In a full application of a process-based approach (Hayes et al., 2006; Hayes & Hofmann, 2017), we might have performed a meta-analytic review of mediation and moderation analyses, and organized the comparison conditions by the methods that elicited processes of change rather than merely those targeting them. We were unable to implement such an approach as few studies have identified their underlying mechanisms of change. Furthermore, there are statistical concerns over group comparison approaches to mediation that suggest a far more idiographic approach may be necessary to properly identify and assess processes of change, and to gather them into population estimates (Hayes, Hofmann, Stanton, Carpenter, Sanford, Curtiss, 2019, in press). The present meta-analysis, therefore, set inclusion criteria for interventions based on their putative targeted processes of interest, not their empirically established change processes. With this in mind, we consider the approach taken in this study as a useful first step toward process-based classification and evaluation of psychosocial interventions organized by functional criteria. Throughout this meta-analysis, we attempted to apply conservative criteria. For one, we only included RCTs that compared PF interventions with first line psychosocial interventions. By excluding wait-list groups, we were able to assess the incremental values of PF interventions above and beyond the initial care clients are likely to receive for SUDs. Finally, we focused only on diagnosable SUDs, which may assist in the external validity of this study's conclusions to the extent that systems of care are organized around these criteria. The present study has several notable limitations and the present findings should be interpreted in light of them. The first limitation was

the subjective nature of judging whether a given third-wave CBT was considered a PF intervention. Informed by Hayes et al. (2011), this decision-making process relied on the description of the study intervention in the published manuscript. In particular, we excluded studies that examined MBCT (Segal et al., 2013) because valuing and behavioral commitments were not specified, and we excluded studies of behavioral activation because neither cognitive defusion nor acceptance targets were explicitly listed. It is possible that these interventions, when delivered, often target both processes of change in-session. It is not uncommon for practitioners to claim they add values and behavior change elements on their own to mindfulness methods such as MBCT. We are unaware of any RCTs that have yet examined this issue in a more formal way, however, and thus this research hole will need to be filled before conclusions can be reached. Second, there were substantial heterogeneities within each intervention group (i.e., PF interventions vs. first line psychosocial interventions) in the format of intervention, duration of the therapy, and researcher allegiance. As such, the interpretation of the present findings warrants extra caution. Third, only three studies had a biological confirmation of substance use while the remaining seven studies used only self-reported substance use/abstinence measures. Fourth, our findings favorable to the PF interventions might have been due to publication biases. Fifth, we did not conduct a cost effectiveness nor a cost benefit analysis and thus, as a policy matter, systems of care will need to judge whether the small increase in effectiveness (a “need to treat” of approximately 11.7 cases to encounter a superior outcome) above firstline treatments is worth the possible additional costs. This is a complex matter, but careful economic analyses exist in the PF literature that have reached positive conclusions (e.g., Finnes et al., 2019; Kemani et al., 2015). Sixth, as noted above, only two studies out of 10 formally measured psychological flexibility processes, and these two studies did not conduct formal mediational analyses. This means that the favorable outcomes found for PF interventions in the present study cannot yet be reliably linked to changes in psychological flexibility. Formal measurement of processes of change and the calculation of indirect effects will need to become more of an expected standard if movement toward a process-based approach is to progress. Furthermore, theoretically coherent processes of change such as psychological flexibility should also be compared with other theoretically distinct processes, such as increasing self-efficacy or decreasing negative affect. A full implementation of a process-based approach arguably requires both assessment and methodological changes in the field, such as the use of ecological momentary assessment, complex network analysis, and nomothetic conclusions based on many idiographic analyses (Hayes et al., 2019, in press). It should be also noted that better treatment implementation rules are needed to help clinicians select and deploy treatment elements based on process of change evidence (Hayes & Hofmann, 2017). The present study suggests that the first major hurdle for PF interventions has been passed, but it is important to move more directly to studies that compare the relative effects of PF interventions to formal psychosocial interventions. There are only a few high quality studies of that kind and thus more research will be required before the relative value of PF interventions in terms of outcomes as part of formal evidence-based practice can be examined. It is important that such studies examine a range of key issues since outcome differences are likely to be small if they exist at all, and a fully useful comparison will involve complex scientific and practical matters beyond outcomes per se, such as ease of training, breadth of application, client and clinician acceptability, maintenance of training effects, cost, processes of change, and the like. Finally, we should note that the dropout rate of PF interventions was 42.7% in this analysis. This rate is comparable to that of first line psychosocial interventions (41.4%) and is characteristic of this population generally. However, it is important not to lose sight of both the analytic and practical problem this presents in terms of possible 118

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attrition. In addition to continuing to identify methods for reducing dropout, greater effort needs to be made to assess outcomes, even if participants drop out from treatment. The present study is the first meta-analysis of RCTs using psychological flexibility as a guiding framework for classifying third-wave CBTs for SUDs and systematically measuring treatment outcomes. Our results suggest that PF interventions as a whole may be useful treatments for SUDs in terms of substance discontinuation and other relevant outcomes. While this study is just a first step, it suggests the possible value of clustering results by the processes of change targeted by psychosocial interventions, as the field of evidence-based therapy moves from a protocol-based past to a more process-based future.

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Conflicts of interest Tatsuo Akechi has received lectures fees from Astellas, AstraZeneca, Daiichi-Sankyo, Dainippon-Sumitomo, Eizai, Hisamitsu, Lilly, MSD, Meiji-seika Pharma, Mochida, Novartis, Otsuka, Shionogi, TanabeMitsubishi, Terumo, and Yoshitomi. Tatsuo Akechi has received research funds from Daiichi-Sankyo, Eizai, MSD, Pfizer, Novartis, and Tanabe-Mitsubishi. Masaki Kondo has received a lecture fee from Yoshitomi pharmaceutical company and a research grant from Novartis pharmaceutical company. Steven C. Hayes and Akihiko Masuda writes books and do training on ACT and psychological flexibility. All other authors declare that they have no conflicts of interest. Role of funding sources Funding for this study was provided by JSPS KAKENHI Grant Number 16K13048. Japan Society for the promotion of science had no role in the study design; the collection, analysis, or interpretation of the data; writing of the manuscript; nor the decision to submit the paper for publication. Acknowledgments The authors would like to thank Dr. Jason B. Luoma for providing data for this study. Appendix Search strategy via MEDLINE (1202) 2016.0720. ((Substance Use Disorders [MeSH]) OR (Alcohol Drinking [MeSH]) OR ((abuse* [tiab] OR abstin* [tiab] OR abstain [tiab] OR addict* [tiab] OR dependen* [tiab] OR misuse [tiab] OR overdose [tiab] OR withdrawal [tiab] OR disorder* [tiab]) AND ((Amphetamines [MeSH]) OR (Cannabis [MeSH]) OR (Cocaine [MeSH]) OR (Designer Drugs [MeSH]) OR (Heroin [MeSH]) OR (Methamphetamine [MeSH]) OR (Narcotics [MeSH]) OR (Street Drugs [MeSH]) OR (alcohol [tiab] OR amphetamine* [tiab] OR drug* [tiab] OR polydrug [tiab] OR substance [tiab] OR cannabis [tiab] OR cocaine [tiab] OR “hash oil*” [tiab] OR hashish [tiab] OR heroin [tiab] OR lsd [tiab] OR marihuana [tiab] OR marijuana [tiab] OR methadone [tiab] OR mdma [tiab] OR morphine [tiab] OR ecstasy [tiab] OR methamphetamine* [tiab] OR narcotics [tiab] OR opioid* [tiab] OR opiate* [tiab] OR opium [tiab])))) AND ((Acceptance and Commitment Therapy [MeSH]) OR (Mindfulness [Mesh]) OR (ACT [tiab] NOT action* NOT activa* NOT active*) OR (Acceptance and Commitment Therapy [tiab] OR mindful* [tiab] OR psychological flexibility [tiab] OR committed action [tiab] OR relational frame theory [tiab] OR contextual behavior* [tiab])) AND (((randomized controlled trial [pt]) OR (controlled clinical trial [pt]) OR (randomized [tiab]) OR (placebo [tiab]) OR (drug therapy [sh]) OR (randomly [tiab]) OR (trial [tiab]) OR (groups [tiab])) NOT (animals [mh] NOT humans [mh])) 119

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