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Cognitive Therapy and Research

, Volume 42, Issue 2, pp 212–217 | Cite as

Miles to Go Before We Sleep: Advancing the Understanding of Psychotherapy by Modeling Complex Processes

  • Lorenzo Lorenzo-Luaces
  • Robert J. DeRubeis
Original Article

Abstract

One of the main debates in the study of psychotherapy is whether specific techniques are best indicated for different problems or whether “common factors” better account for the efficacy of psychotherapy. Evidence for the superiority of specific techniques is mixed and limited to a handful of diagnoses. By contrast, evidence for the importance of common factors is riddled with methodological weaknesses and may be of limited clinical utility. The stagnation in this debate may reflect that the research methods heretofore employed have reached a plateau in their ability to advance knowledge regarding psychotherapy processes. The articles of the special issue move beyond simple bivariate relationship and attempt to model the real-world complexity involved in the process of psychotherapy. It is argued that these types of investigations, which model the interactions of patient characteristics as well as multiple specific and “common factors,” are the best way to advance the state of knowledge regarding psychotherapy processes.

Keywords

Psychotherapy processes Psychotherapy outcome Psychotherapy 

The prototypical psychotherapy process article begins by declaring that psychotherapy “works” while lamenting that its mechanisms of change are not well-understood. The articles on the Special Issue manage to avoid this convention, which is no small feat. More impressively, this series of studies illustrate significant advancements in psychotherapy research. The commonality of these studies is that they attempt to model, theoretically or statistically, the real-world complexity involved in the process of psychotherapy. Another impressive commonality of the papers is that they suggest actionable recommendations for clinicians.

Psychotherapy researchers can be arrayed along a dimension, which, at one extreme, features those who espouse the importance of applying specific treatments and techniques for different diagnoses. These researchers usually rely on evidence from randomized clinical trials (RCTs). At the other extreme are those who propose a central role of “common factors” as causal agents of change, with evidence drawn primarily from correlational (process–outcome) findings. Beyond supporting the superiority of cognitive-behavioral therapies (CBTs) for bulimia/binge-eating and sleep disorders (see DeRubeis and Lorenzo-Luaces 2016; Hofmann and Barlow 2014), as well as the importance of engaging feared stimuli in anxiety disorders (see Laska et al. 2014), the RCT literature does not support very specific or helpful claims about how best to conduct psychotherapy. In depression, arguably the disorder for which psychotherapy has been most widely studied, evidence for differences in outcome among these orientations is weak, though the quality of the evidence varies widely between schools of therapy (Barth et al. 2013). Across disorders, a published meta-analysis found that the linear association between adherence to specific elements of therapeutic orientations and outcomes was small and not statistically significant (Webb et al. 2010). A subsequent unpublished meta-analysis (Soriano 2014) did find a relationship between adherence and outcome as well as between competence and outcome. In the latter study, these relationships appeared concentrated in studies of treatments for substance use and for depression.

Research on common factors is somewhat more optimistic in that it supports a robust bivariate association between the therapeutic alliance and outcomes (Horvath et al. 2011). However, very few of these studies have modelled the temporal association between alliance and outcomes properly, and those studies that have done so have produced inconsistent findings (see Webb et al. 2014). Even when significant alliance–outcome relations are obtained in studies that rule out temporal confounds, the observed effects have tended to be smaller than when the temporal confounds are not ruled out (e.g., Falkenström et al. 2013).

If the old claim “psychotherapy works, we just don’t know how” is to be replaced, its most defensible replacement would be something like “psychotherapy works, probably through the working alliance, at least a little, and maybe through specific factors, at least for specific problems.” Not exactly the second law of thermodynamics, but it must do for now. To be sure, this is preferable to a time in which the study of mechanisms of change was a moot point because there were doubts about whether psychotherapy was effective at all (Dawes 1994). However, the methods heretofore employed, bivariate associations between process and outcomes and comparisons of mean differences between treatments, have reached a kind of plateau in their ability to advance knowledge regarding processes and outcomes of psychotherapy. The type of studies most likely to advance our understanding of the workings of psychotherapy, as exemplified by the articles of the special issues, are those that isolate effective ingredients, be they “common” or “specific” for different types of patients (Hofmann and Barlow 2014).

Leveraging the Working Alliance to Maximize Outcomes

The association between the working alliance and outcomes has long been assumed be causal, such that a better working alliance leads to better treatment outcomes, and poorer alliances lead to poorer outcomes. It is not often appreciated that the validity of this interpretation of the research and its utility are two separate matters (Kendell and Jablensky 2003). In other words, even if it were the case that, on average, a positive working alliance leads to good outcomes, the knowledge of this simple alliance–outcome association is not helpful on a patient-by-patient basis, much less on a session-by-session one. As several studies have suggested, the working alliance may promote symptom change to differing degrees among different clients (Lorenzo-Luaces et al. 2014), across different forms of therapy (Arnow et al. 2013), at different times in therapy (Webb et al. 2014), and among different clients undergoing different types of therapy (Lorenzo-Luaces et al. 2017). Thus, even if the alliance predicts to outcome in general, it may not do so in all cases and it may not be clear what to do about it. The two articles by Zilcha-Mano et al. (2018b) exemplify just how complex the alliance–outcome association might be.

Across two different samples, Zilcha-Mano and colleagues collected multiple assessments of the Working Alliance Inventory (WAI) on which eight of the 12 items refer to patient-therapist agreement in the goals and tasks of therapy, while the remaining four items capture the affective bond. With multiple assessments across time for each patient-therapist dyad, within-person session-to-session change on the WAI can be modeled. Using within-person change as a predictor of outcomes has several advantages over using the WAIs across therapy or using a single WAI as an indicator of the alliance. It addresses both the temporal order of change, because change in WAI in one session can be associated to subsequent symptom change, as well as potential third-variable confounds which also vary between patients (Sasso et al. 2016). Zilcha-Mano et al. (2018a, b) draw on techniques from machine-learning to identify patient features that may moderate the alliance–outcome association. Their analyses suggest a two-way interaction of intrusiveness and coldness in moderating the effect of the alliance on outcomes. The alliance was related to outcomes among patients who were rated as not overly intrusive but who were overly cold. As the authors state, in this group, which represented about a fifth of the sample, true “curative” effects of the alliance on outcome might be present.

In the second study, Zilcha-Mano et al. (2018a) explore whether the effect of changes in the alliance on subsequent symptoms varies according to a time-varying predictor: changes in the patient’s perception of their quality of life. In sessions in which the levels of life satisfaction had been rated as low to moderate, the WAI did not predict subsequent symptoms. However, in sessions in which quality of life ratings were high, the alliance predicted subsequent symptom improvement. This article, the first of its kind, represents one of the most clinically useful studies in the literature on the alliance–outcome association because it provides information about when it may be most beneficial to focus on the alliance.

The study by Sauer-Zavala et al. (2018) adds that the alliance may work with treatment-specific factors as well as with other so-called common factors. These authors examined the association between pre-treatment expectancies for positive improvement, alliance, and treatment outcomes in two versions of cognitive-behavioral therapy (CBT) for anxiety. Prior research suggested that the early alliance mediates the association between pretreatment expectancy and outcomes (Joyce et al. 2003). The interpretation of these findings is that individuals who come to therapy with positive expectations do better, at least in part, because they develop good working alliances with their therapist. Sauer-Zavala et al. find evidence that this is true in disorder-specific CBT treatment protocols but not in treatment guided by Barlow’s Unified Protocol (UP). This finding was more robust with the affective bond subscale of the WAI. The authors conjecture that lower expectations for treatment may lower alliance ratings in the single-disorder treatment insofar as clients experience therapists as not having a full grasp on them as individuals and being too focused on diagnostic-specific issues. In the UP, the effects of expectations and alliance appear to be independent of each other. Three-way variable relations such as these are difficult to comprehend but they may have a better chance of reflecting a complex reality than do simpler bivariate associations.

Whereas the articles by Sauer-Zavala et al. (2018) and the two articles by Zilcha-Mano and colleagues highlight that sources of complexity in the conduct and study of psychotherapy are not always obvious, Crawford et al.’s (2018) review is a pleasant reminder that sometimes they are. They focus on how the patient’s age and developmental stage are and should be primary considerations in how anxiety treatment is delivered. Anxiety in children is a significant public health problem as it is a predictor of subsequent anxiety, mood, and substance use disorders (Brückl et al. 2007). As the authors state plainly, it is not enough to import the CBT model to youngsters without a proper consideration of how their developmental stage affects the delivery, as well as the study, of psychotherapy processes. Regarding the latter, the authors posit that inconsistent findings on the role of homework in CBT for youth with anxiety may be explained by unreliability in children’s assessment of homework completion. Regarding the former, the authors highlight the importance of delivering CBT in a collaborative “coaching” style as opposed to a didactic “teacher-like” fashion. As well, it cannot be assumed that the treatments that are efficacious for adults need to be completely revamped for work with children. Until recently it was assumed that exposure tasks could only be introduced in therapy with youth after significant psychoeducation, socialization to treatment, and teaching of coping skills. This assumption has been corrected by the results of studies in which exposure tasks are implemented early in therapy without increased dropout or lower efficacy rates.

Taken together, the articles by Zilcha-Mano et al. (2018a, b), Sauer-Zavala et al. (2018), and Crawford et al.’s (2018) highlight how so-called “common factors” in treatment are a misnomer. Expectancies and the therapeutic alliance may occur in all forms of therapy and may be measured within all patients. However, they are multidimensional constructs and occur in the context of other “common factors” and specific therapy techniques (Hofmann and Barlow 2014). They do not unequivocally account for outcomes for all patients across all therapy types.

Studying “Specific” Treatment Processes in Context

Doss (2004) called attention to the fine-grained nature of therapeutic interactions by distinguishing treatment processes that therapists engage in (e.g., Socratic questioning) from the change processes in patients they are meant to activate (e.g., generating evidence for and against a belief). Further, these change processes are meant to engage and alter psychological mechanisms (e.g., changes in beliefs, development of reappraisal skills) that are expected to facilitate outcomes (e.g., a reduction in depression). This characterization of the different processes makes plain that in-session behaviors are rather distal from treatment outcomes, which makes it unsurprising that it has been so difficult to identify specific therapist behaviors on the part of the patient or therapist that have strong and consistent associations with outcomes. Thus, by refining when and how change is being measure in this context, we can come closer to our understanding of therapeutic change.

Westra and Norouzian (2018) authors argue that the way in which psychotherapists respond to reactance/resistance predicts outcomes reliably (Beutler et al. 2011). Focusing on within-session treatment processes, they review the literature on motivational interviewing (MI) techniques, which are indicated in the context of reactance/resistance. Specifically, high levels of reactance from the patient are best met by a non-directive supportive stance by the therapist (vs. a directive or confrontational one). This observation is germane to CBTs, which are discussed stereotypically as the most directive of the psychotherapies. There is evidence that CBT therapists respond to resistance by becoming less adherent to CBT and drawing on other interventions (Zickgraf et al. 2016). MI provides concrete supportive techniques for responding to resistance in ways that can be consistent with the change-based goals of CBTs. The authors propose that resistance is a “process marker” in CBT, functioning as a kind of thermometer for client’s willingness to change, and propose that MI interventions can facilitate the subsequent use of more traditional CBT interventions.

Hoet et al. (2018) refine the measurement of change processes in patients by using ecological momentary assessments (EMA) probing day-to-day use of coping skills. In their sample of patients undergoing CBT or self-system therapy (SST), participants reported utilizing coping skills consistent with the therapy they were undergoing: CBT skills in CBT; SST skills in SST. On days in which these skills were used participants showed better mood and improved functioning. These findings comport with the intuition that two different therapies (here CBT and SST) can produce roughly equivalent outcomes by engaging different treatment processes (DeRubeis et al. 2005).

Renna et al. (2018) focus on attention regulation as a psychological mechanism involved in emotion-regulation therapy (ERT) for generalized anxiety disorder (GAD). The authors ground their discussion of attention regulation in the context of research on mindfulness and other emotion-regulation skills. Measuring attention control via cognitive tasks, they found that, relative to controls, patients diagnosed with GAD exhibited more difficulty with conflict adaptation (i.e., the use of cognitive control to select among incongruent emotional information) as well as with emotional interference (i.e., the ability to inhibit task-irrelevant negative emotional information). While both conflict adaptation and inhibiting emotional interference improved over the course of treatment, only the inhibiting of emotional interference was associated with therapeutic outcomes. Inhibiting emotional interference was related to a hypothesized mindfulness construct, non-reactivity to internal experience, though it was not related to observing internal experience. The results of the study highlight that not every potential variable in which patients differ from controls is a causal agent relevant to treatment. As well, the findings underscore that mindfulness, an increasingly popular concept in the psychological literature, is not unidimensional.

Another consistent finding from the process literature is that providing therapists with feedback on patient outcomes is associated with improvement, at least for patients who are not improving at an expected rate (Lambert and Shimokawa 2011). Hooke et al. (2018) tested patient’s perceptions of the way in which feedback is presented. Although this is a relatively circumscribed experiment, the fact that the manipulation is so specific provides a concrete and refined test of how to deliver progress feedback. The results of their study suggest that, on average, feedback connected to expected recovery curves is perceived by client as more useful than feedback without the expected recovery curves.

Turtles All the Way Down

As the articles of the special issue suggest, psychotherapy research has become increasingly refined. The statistical and methodological innovations compensate, to some extent, for the fact that many of the variables we are interested in are not subject to experimental manipulation. Still, much work remains to be done. Although discoveries in the way of statistically significant findings expand our knowledge, they also create more questions regarding the conditions in which psychotherapy can be effective. For example, the discovery that providing therapists with feedback about patient outcomes results in more improvement than not providing such feedback (Fig. 1a) raises the question as to how this effect is mediated. When a relevant mediator (e.g., increasing goal consensus) is found (Fig. 1b), two questions emerge: (1) under what conditions does providing feedback increase goal consensus?; and (2) what accounts for the effect of goal consensus on outcomes? One might find that providing feedback improves goal consensus because it leads therapists to use more supportive MI-type interventions (Fig. 1c) and that such goal consensus leads to improved outcomes by fostering behavioral change (Fig. 1d). These findings, in turn, need to be elaborated on. This stylized depiction of the effects of feedback may not appear to be very complex, including as it does only five variables. While feedback condition is a static variable, experimentally manipulated, the other four variables would ideally be measured and modelled as within-person change, to account for the temporal order, and would therefore require multiple assessments of these constructs. The model in Fig. 1, however, does not account for between-patient differences. It may, for example, apply well to patients who are not progressing in therapy as expected, yet it may have little relevance for those who are progressing as expected. Sample size, measurement error, variability, missing data, and other factors further complicate such an enterprise.

Fig. 1

Hypothetical associations between outcome feedback (yes/no), motivational interviewing techniques (MI), early goal consensus, behavioral change, and symptom change. Black lines indicate direct relations. Dashed lines indicate partial mediation by preceding variable. a Association between outcome feedback and symptom change. b Partial mediation of the effect on Panel A by goal consensus. c Partial mediation of the effect of feedback on goal consensus by the use of MI techniques. d Partial mediation of the effect of goal consensus on symptom change by behavioral change

It is encouraging that the complexity in psychotherapy can be accounted for, at least to some extent. Given the interest in moderators of treatment outcomes and process–outcome correlations, future research studies should include measures of many potentially informative baseline covariates (Kessler et al. 2017). Predictive research of this nature requires sample sizes that are substantially larger than are typical of psychotherapy research. Capitalizing on information gathered from patients in the context of naturalistic medical practices, for example by integrating outcome measures into electronic systems, is one way to achieve larger sample sizes (Chekroud 2017). Although naturalistic research of this nature is less tightly-controlled than standard RCTs, the increased variability in outcomes and therapy delivery may facilitate rather than limit process research (DeRubeis et al. 2014b). Within these and other contexts, the measurement of therapy processes can be streamlined, made richer and more ecologically valid with the use of mobile devices, which continue to become more user-friendly. Increasingly sophisticated statistical approaches will be required. Data mining (DeRubeis et al. 2014a; Huibers et al. 2015) and machine learning approaches (Lorenzo-Luaces et al. 2017; Zilcha-Mano et al. 2018a) have already shown promise in allowing psychotherapy researchers to ask and answer questions that had previously been very difficult to address. Although a comprehensive model of how psychotherapies work will probably remain elusive for some time, if the articles of the Special Issue are any indication we are encouraged by the directions the field is taking.

Notes

Compliance with Ethical Standards

Conflict of interest

Lorenzo-Luaces and DeRubeis declares that they have no conflict of interest.

Research Involving Human Participants and/or Animals

This article does not contain any studies with human participants performed by any of the authors.

Informed Consent

This article does not contain any studies with human participants and thus no informed consent is required.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Psychological and Brain SciencesIndiana University – BloomingtonBloomingtonUSA
  2. 2.Department of PsychologyUniversity of PennsylvaniaPhiladelphiaUSA

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