a focus on positive psychology interventions

Cognitive Behaviour Therapy

ISSN: 1650-6073 (Print) 1651-2316 (Online) Journal homepage: http://www.tandfonline.com/loi/sbeh20

A topographical map approach to representing treatment efficacy: a focus on positive psychology interventions Eugenia I. Gorlin , Josephine Lee & Michael W. Otto To cite this article: Eugenia I. Gorlin , Josephine Lee & Michael W. Otto (2017): A topographical map approach to representing treatment efficacy: a focus on positive psychology interventions, Cognitive Behaviour Therapy, DOI: 10.1080/16506073.2017.1342173 To link to this article: http://dx.doi.org/10.1080/16506073.2017.1342173

Published online: 31 Jul 2017.

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Date: 11 August 2017, At: 06:00

Cognitive Behaviour Therapy, 2017 https://doi.org/10.1080/16506073.2017.1342173

A topographical map approach to representing treatment efficacy: a focus on positive psychology interventions Eugenia I. Gorlin , Josephine Lee and Michael W. Otto Department of Psychological and Brain Sciences, Boston University, 648 Beacon St., #6, Boston, MA 02215, USA

Downloaded by [Eugenia Gorlin] at 06:00 11 August 2017

ABSTRACT

A recent meta-analysis by Bolier et al. indicated that positive psychology interventions have overall small to moderate effects on well-being, but results were quite heterogeneous across intervention trials. Such meta-analytic research helps condense information on the efficacy of a broad psychosocial intervention by averaging across many effects; however, such global averages may provide limited navigational guidance for selecting among specific interventions. Here, we introduce a novel method for displaying qualitative and quantitative information on the efficacy of interventions using a topographical map approach. As an initial prototype for demonstrating this method, we mapped 50 positive psychology interventions targeting well-being (as captured in the Bolier et al. [2013] meta-analysis, [Bolier, L., Haverman, M., Westerhof, G. J., Riper, H., Smit, F., & Bohlmeijer, E. (2013). Positive psychology interventions: A meta-analysis of randomized controlled studies. BMC Public Health, 13, 83]). Each intervention domain/subdomain was mapped according to its average effect size (indexed by vertical elevation), number of studies providing effect sizes (indexed by horizontal area), and therapist/client burden (indexed by shading). The geographical placement of intervention domains/subdomains was determined by their conceptual proximity, allowing viewers to gauge the general conceptual “direction” in which promising intervention effects can be found. The resulting graphical displays revealed several prominent features of the well-being intervention “landscape,” such as more strongly and uniformly positive effects of future-focused interventions (including, goal-pursuit and optimism training) compared to past/ present-focused ones.

ARTICLE HISTORY

Received 16 January 2017 Accepted 10 June 2017 KEYWORDS

Positive psychology interventions; dissemination; efficacy; treatment selection; graphical displays

Introduction Positive psychology interventions are designed to enhance positive emotions and pleasure, engagement in life, and meaning in life (Seligman, Steen, Park, & Peterson, 2005). The focus and aims of these interventions can be differentiated from that of most mental health interventions, which are targeted toward the amelioration of suffering rather than promotion of well-being (Seligman & Csikszentmihalyi, 2000), although some well-being CONTACT Eugenia I. Gorlin [email protected] University, 648 Beacon St., #6, Boston 02215, USA. © 2017 Swedish Association for Behaviour Therapy

Department of Psychological and Brain Sciences, Boston


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protocols have specifically and successfully targeted the treatment of residual symptoms of anxiety and depression with a well-being approach (e.g. Fava, Rafanelli, Cazzaro, Conti, & Grandi, 1998; Fava et al., 2005). A recent meta-analysis of 39 controlled trials of positive psychology interventions indicates that positive-psychology interventions offer reliable benefits, reflecting small to moderate effect sizes (d = .20–.34) for subjective and psychological well-being outcomes (Bolier et al., 2013). Yet, there is considerable heterogeneity within these estimates, perhaps reflecting the high variability of interventions in terms of their content and duration. For example, interventions range from single-session, computer-assigned writing assignments to promote optimism (e.g. Peters, Flink, Boersma, & Linton, 2010) to more comprehensive programs of cognitive-behavioral solution-focused coaching delivered by therapists over a multi-week period (e.g. Green, Oades, & Grant, 2006; Spence & Grant, 2007). Accordingly, meta-analytic results provide encouragement about the potential value of offering well-being interventions, but do not offer much guidance about the specific interventions that hold particular promise for further randomized evaluation and clinical application. In this article, we introduce a new method for communicating the potential benefit of interventions, utilizing effect sizes to graphically represent the strength of core intervention domains as well as more specific sub-domains (as captured by low-resolution and high-resolution map displays, respectively) relative to one another. In this topographical map approach, the height of an intervention type is determined by its controlled effect size (Cohen’s d; Cohen, 1988) and the area assigned to the intervention type represents the number of studies on which the effect size estimate is based. Hence, greater area provides a general index of greater confidence in an effect size estimate. The geographical placement of intervention types on each map in terms of conceptually similar intervention methods (e.g. interventions involving cognitive reappraisal are grouped closer to each other than to interventions involving guided imagery) is designed to provide qualitative information regarding the general conceptual “direction” in which beneficial outcomes can more consistently be found, encouraging innovation in clinical interventions as guided by extant outcome findings (e.g. to provide guidance on not only the exact interventions that have shown benefit, but the general conceptual elements that these specific interventions appear to share in terms of therapeutic content and targeted mechanisms). Notably, “valleys” (low effect sizes) assigned to intervention types within or near a generally efficacious region also provide information on where protocols may have gone wrong in terms of providing beneficial outcomes. Finally, to allow consideration of the efficacy of interventions relative to both clinician and client burden, an ordinal scale representing burden (see Method) is provided in terms of the degree of shading of interventions. The designed result is a map indicating movements clinicians can take to offer efficacious well-being interventions within the overall domain of positive-psychology interventions captured in a meta-analytic review (Bolier et al., 2013). In providing this information graphically as well as textually, we are adhering to recommendations for the use of graphical displays to aid the interpretation and integration of complex information (Vekiri, 2002).

Method For the construction of these low- and high-resolution topographical maps (Figures 1 and 2, respectively), we used data from the meta-analysis by Bolier et al. (2013), with the following


COGNITIVE BEHAVIOUR THERAPY

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Figure 1. “Low-resolution” topographical map of six core positive-psychology intervention domains.

Figure 2. “High-resolution” topographical map of 20 positive-psychology intervention subdomains. Notes: A1: Re-experiencing Positive Events; A2: Positive Event Listing and Causal Analysis (“Three Good Things”); A3: Humorous Event Listing and Causal Analysis; A4: Positive Self-Statements; A5: Positive Thinking with Hope-Based Cognitive Restructuring; A6: Self-Compassion Training; A7: Sacred Moments; A8: Savoring; B1: Optimism; B2: Optimism + Planning; C1: Listing Personal Gratitudes; C2: Expressing Gratitude to Others; C3: Expressing Gratitude to Others + Positive Event Listing; D1: Strength Identification and Utilization (“Using strengths in a new way”); D2: Strength Identification + Cognitive Restructuring; E1: Action Coaching w/Self-Monitoring & Error Correction; E2: Action Coaching w/ Values Clarification; E3: Action Coaching + Cognitive Restructuring; E4: Action Coaching + Hope Visualization; F1: Acts of Kindness.


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exceptions. Where Bolier and associates collapsed effect size estimates across several distinct active intervention conditions (e.g. data from Gander, Proyer, Ruch, & Wyss, 2012; Sheldon & Lyubomirsky, 2006), we recomputed effect sizes for each individual intervention condition (relative to the placebo or waitlist control condition). We excluded data from two studies for which individual intervention effect sizes were unavailable. Also, for outcome we used measures of either subjective well-being or psychological well-being, and took the average of these measures when both were provided. Finally, we provide effect sizes only for studies of non-clinical populations to provide greater uniformity for estimates (36 of 39 studies analyzed by Bolier et al., 2013). These inclusion procedures yielded a total of 50 individual intervention conditions across 36 studies that were available for analysis. Card sorting procedures (Righi et al., 2013) were used to classify these 50 individual interventions into core intervention domains. Two independent clinical raters sorted descriptions of each intervention, and then met to resolve differences in ratings (5 of 50 cards sorted). These sortings led to six core domains of positive-psychology interventions. Then in a subsequent sorting procedure (conducted through joint discussion by the first and third authors), these six domains were broken into 20 discrete sub-domains to allow specific characterization of individual intervention types. Table 1 provides information on the resulting domains and subdomains. These categories were then arranged on the lowand high-resolution 3D topographical maps presented in Figures 1 and 2 (respectively), with the horizontal area determined by the number of studies and the height determined by the average effect size for that domain/subdomain. The time burden of the intervention was then represented ordinally by the degree of shading (see scale at the bottom of Table 1); interventions requiring greater amounts of therapist time received the darkest shading, followed by self-help interventions that required greater versus lesser amounts of client time/effort. For additional clarity, the “low-resolution” map displayed in Figure 1 provides averaged effect sizes across the six core domains of interventions, whereas the “highresolution” map displayed in Figure 2 provides a finer-grained depiction of the efficacy of the 20 individual intervention sub-domains, still arranged in terms of their relative conceptual proximity to each other (both within and across the core domains).

Results and discussion This paper introduces a novel method for translating effect size information into a clinically useful “topographical map” that depicts the relative benefits and burdens of specific intervention approaches, thus aiding decisions about further research and treatment selection. In the current report, we mapped the topography of positive psychology interventions targeting well-being, revealing that the highest “peaks” (i.e. largest effect sizes) did not always correspond to the heaviest shading (i.e. highest clinician/client burden). As represented by the maps in Figures 1 and 2, Positive Processing of Future Events (Optimism) and GoalPursuit interventions were associated with the overall most promising effect sizes, with strong uniformity for both sets of interventions (e.g. no peaks or valleys within the domain of Goal-Pursuit). Gratitude interventions also had uniform estimates, suggesting that any of a number of approaches led to the same, albeit small, and benefit. In contrast, hills and valleys of efficacy tended to be the rule for Positive Processing of Past and Present Events and Strength Identification interventions, providing caution to clinicians that efficacy may be dependent on the exact intervention used within these domains, with benefits generally

Domain/Subdomain A. Positive Processing of Past and Present Events A1. Re-experiencing Positive Events: Participants are asked to write/think about one or more positive events that occurred and re-experience them as vividly as possible A2. Positive Event Listing and Causal Analysis (“Three Good Things”): Participants are asked to write down three things that had gone well for them on a given day, and an explanation of why those things happened to them A3. Humorous Event Listing and Causal Analysis: Participants are asked to write down the three funniest things they did or experienced on a given day, and an explanation of why those things happened to them A4. Positive Self-Statements: Participants are asked to relax and clear their minds, focus on their breathing, and rehearse a series of positive self-feeling statements (e.g. “I am a good sort of person,” etc.) A5. Positive Thinking with Hope-Based Cognitive Restructuring: Participants are asked to think about a situation in which a negative event led to unforeseen positive outcomes, with subsequent rehearsal of this memory A6. Self-Compassion Training: Participants are asked to think about an event that made them upset, and write themselves a letter expressing compassion for this episode A7. Sacred Moments: Participants are asked to focus on their breath, sanctify a personal object, and mindfully appreciate the sacredness of the moment A8. Savoring: Participants are asked to practice savoring strategies (e.g. self-congratulations on positive events, expressing positive emotions), recall positive events from the week and write about how they could have been savored, and keep a daily savoring log B. Positive Processing of Future Events (Optimism) B1. Optimism: Participants are asked to write/think about their best-possible life in the future, imagining that everything had gone as well as it possibly could (with or without active consoling of the present) B2. Optimism + Planning: Participants are asked to write about their best-possible life in the future, plus write down a goal or goals to help them achieve the imagined outcomes C. Gratitude Domain C1. Listing Personal Gratitudes: Participants are asked to write down things in their life that they are grateful for, and to practice making such a list repeatedly C2. Expressing Gratitude to Others: Participants are asked to write a letter to an individual to whom they are grateful but have not fully expressed this gratitude C3. Expressing Gratitude to Others + Positive Event Listing: Participants are asked to write a letter to an individual to whom they are grateful (as in C2 above), as well as write down three things that had gone well for them each day (as in A2 above) D. Strength Identification Domain D1. Strength Identification and Utilization (“Using strengths in a new way”): Participants identify and prioritize perceived strengths from a list, with assigned homework to utilize top strengths during a subsequent time period, with or without diary-based monitoring of use D2. Strength Identification + Cognitive Restructuring: Participants are trained to identify and enhance their resilience skills through a cognitive-behavioral framework E. Goal Pursuit Domain

Table 1. Domain and sub-domain classification of the 50 positive psychology interventions drawn from Bolier et al. (2013).


Mean therapist/ client burdena SH2 SH1 SH1 SH2 SH3 SH2 SH2 SH2 SH4 SH2 SH2 SH2 SH3 SH4 SH1 SH3 SH3 SH2 T3 T3

Number of effect sizes 14 5 3 1 1 1 1 1 1 8 7 1 7 4 2 1 4 3 1 13

(Continued)

.32

.06

.17 .20

.31

.21

.20 .19

.37

.36 .36

.28

.02

.06

.07

.40

−.07

.13

Mean effect size .17 .25

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Mean therapist/ client burdena T4 T3 T4 T1 SH3 SH3

Number of effect sizes 6 3 2 2 4 4

.31 .31

.30

.32

.41

Mean effect size .28

SH = Self-help intervention, T = therapist intervention. SH1 ≤ 60 min; SH2 = 60–119 min; SH3 = 120–179 min total; SH4 = 180 + min. T1 ≤ 60 min; T2 = 60–119 min; T3 = 120–179 min; T4 = 180 + min.

a

Domain/Subdomain E1. Action Coaching w/ Self-Monitoring & Error Correction: These interventions trained participants to set realistic goals and develop action plans, plus monitor and evaluate goal progression, making adjustments as necessary. Some interventions also included identifying personal strengths and building self-efficacy E2. Action Coaching w/ Values Clarification: These interventions trained participants to set realistic, values-based goals, develop action plans, and savor or cultivate positive, “flow”-like experiences E3. Action Coaching + Cognitive Restructuring: These interventions trained participants to set realistic goals and develop action plans, plus restructure negative cognitions that threaten to undermine successful goal-pursuit E4. Action Coaching + Hope Visualization: These interventions trained participants to set realistic, personally meaningful goals, develop action plans, plus engage in detailed guided visualization during which they reflected on the personal importance, value, and feelings associated with their goals F. Acts of Kindness Domain F1. Acts of Kindness: Participants are asked to do and/or record acts of kindness performed daily

Table 1. (Continued).


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on the order of small effect sizes. For example, within the Positive Processing of Past and Present Events domain, interventions emphasizing more vivid, detailed re-experiencing or savoring of positive events seem to have more promise than those emphasizing causal analysis or events listing (consistent with research showing greater mood effects for more concrete, experiential processing of positive events relative to more analytical strategies; see Lyubomirsky, Sousa, & Dickerhoof, 2006). The potential usefulness of a topographical map approach is supported by information processing research. Vekiri (2002) reviewed three complementary perspectives from information processing theory that inform when and how graphical displays can aid in the learning and synthesis of complex data. First, the visual argument hypothesis emphasizes that processing visuo-spatial information arranged using unambiguous perceptual symbols—as in the case of a topographical map with pre-defined referents—is less cognitively demanding than processing the same information in text. Second, dual coding theory highlights the largely independent roles of verbal and non-verbal (i.e. imagery-based) information processing systems, which can operate in parallel and offer distinct advantages: for instance, the nonverbal/imagery-based system organizes information in a synchronous manner, allowing many features of a given referent to be represented simultaneously, whereas the verbal system is limited by the constraints of sequential processing. Finally, the conjoint retention hypothesis builds on the proceeding theories and applies them directly to the domain of map learning: according to this account, map displays provide a unique additive processing advantage by allowing for the simultaneous representation of discrete visual features (such as, size, shape, color) and structural relationships (such as, relative distance and boundary relations). As such, map displays allow for complex information to be generated and stored in the form of single holistic units, thus, minimizing cognitive load (Larkin & Simon, 1987; Kulhavy et al., 1993, 1994). As represented in the Figures, the topographical map of the efficacy of positive psychology interventions provides an integrated summary of the degree of efficacy (height), degree of confidence (area), burden (shading), and consistency of interventions (peaks and valleys), providing an integrated suggestion of the “direction” clinicians may want to travel in terms of selecting a positive psychology intervention to test and potentially implement in clinical samples. Figure 1 provides averaged information that is one step more detailed than overall meta-analytic summaries, instead providing efficacy information for groups of interventions that share core similarities. Confidence in the averaged information is conveyed in the width and breadth of the “hill” representing each effect. By contrast, the “high-resolution” map presented in Figure 2 provides more direct evidence for the variability of estimates within each core domain. It requires more information processing due to the greater number of hills, but also provides more fine-grained information for selecting a specific intervention type. The perspective across these two figures is exactly that which we want to convey: a visual map that helps researchers and clinicians choose the strongest options from an array of interventions that differ with respect to content, burden, reliability, and efficacy. The topographical map approach may also aid retention of efficacy information, given the evidence that both imagery and verbal processing can aid retention (e.g. Kosslyn, Holyoak, & Huffman, 1976). Additionally, the salience of particular map features may aid the processing of information that might otherwise get neglected when making treatment selection decisions. For example, visual display of the relative stability (i.e. number of aggregated estimates) within a given intervention domain makes this a more readily transparent and salient feature that can guide clinicians’ decision-making. This offers a potential antidote

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against the kinds of salience biases that can skew providers’ treatment selection decisions in favor of novel but non-robust intervention effects (e.g. Hamill, Wilson, & Nisbett, 1980).


Limitations and conclusions Whereas, we deliberately chose the positive psychology domain for our initial demonstration of the topographical mapping approach, given the conceptual heterogeneity and limited meta-analytic research in this area to date, there were several limitations inherent in the choice of this intervention domain. First, the vast majority of positive psychology intervention trials are conducted with healthy, unselected samples (which thus, served as an inclusion criterion for the present report), which limits the inferences that clinicians can make in applying these interventions to clinical populations. As such, we expect that these maps will primarily be useful to clinical researchers in deciding which well-being-focused intervention strategies are worth testing in clinical samples. Second, these intervention studies vary quite widely in their experimental designs (see Bolier et al., 2013, for details), and are generally optimized for testing the immediate emotional and psychological effects of short, simple positive psychology exercises, rather than larger-scale interventions. Most of these exercises, with the exception of those in the Goal Pursuit domain, are extremely brief (on the order of 15 min to a few hours) and delivered in a self-help format, with well-being typically assessed via self-report measures administered immediately pre- and post-treatment.1 Unsurprisingly, the resulting effect sizes are quite variable and generally more modest than those reported for evidence-based cognitive-behavior therapy for clinical samples (e.g. Hofmann, Asnaani, Vonk, Sawyer, & Fang, 2012). In the present report we aggregate individual effect sizes into broader intervention domains to provide more stable estimates, while also giving an indication of the potentially variable strengths of individual interventions and subdomains. In sum, this novel graphical display methodology shows promise for effectively translating research findings from controlled trials into a clinically useful, easy-to-navigate treatment selection guide.

Note 1. Of note, however, the 10 studies that included three- to six-month follow-up measures did find small but significant effects of the positive psychology intervention at follow-up, per Bolier et al. (2013).

Disclosure statement No potential conflict of interest was reported by the authors.

ORCID Eugenia I. Gorlin

http://orcid.org/0000-0003-0219-901X

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