The Psychotherapy Practice Research Network (PPRNet) blog began in 2013 in response to psychotherapy clinicians, researchers, and educators who expressed interest in receiving regular information about current practice-oriented psychotherapy research. It offers a monthly summary of two or three published psychotherapy research articles. Each summary is authored by Dr. Tasca and highlights practice implications of selected articles. Past blogs are available in the archives. This content is only available in English.
…I blog about psychotherapy for borderline personality disorder, capacity to metnalize and therapy resistant depression, and negative effects of psychotherapy
Type of Research
- ALL Topics (clear)
- Alliance and Therapeutic Relationship
- Anxiety Disorders
- Attendance, Attrition, and Drop-Out
- Client Factors
- Client Preferences
- Cognitive Therapy (CT) and Cognitive-Behavioural Therapy (CBT)
- Combination Therapy
- Common Factors
- Depression and Depressive Symptoms
- Efficacy of Treatments
- Feedback and Progress Monitoring
- Group Psychotherapy
- Illness and Medical Comorbidities
- Interpersonal Psychotherapy (IPT)
- Long-term Outcomes
- Neuroscience and Brain
- Outcomes and Deterioration
- Personality Disorders
- Placebo Effect
- Practice-Based Research and Practice Research Networks
- Psychodynamic Therapy (PDT)
- Resistance and Reactance
- Self-Reflection and Awareness
- Suicide and Crisis Intervention
- Therapist Factors
- Transference and Countertransference
- Trauma and/or PTSD
- Treatment Length and Frequency
Do Common Factors Matter in Psychotherapy?
Cuijpers, P., Driessen, E., Hollon, S. D., van Oppen, P., Barth, J., & Andersson, G. (2012). The efficacy of non-directive supportive therapy for adult depression: a meta-analysis. Clinical psychology review, 32(4), 280-291.
The research evidence indicates that there is very little difference between different types of psychotherapy (CBT, IPT, PDT, EFT, and others) in patient outcomes, especially for depression. Nondirective supportive treatment (NDST) also shows positive outcomes for various disorders. NDST is often used as a “placebo” condition in psychotherapy trials to control for common or non-specific factors. Common factors refer to those aspects that are common to all therapies, but that are not specific to any one therapy (e.g., therapist interpersonal skills, therapeutic alliance, client expectations). NDST does not involve specific therapeutic interventions like cognitive restructuring, transference interpretations, two-chair techniques, etc. In this meta analysis, Cuijpers and colleagues assessed those randomized controlled trials for depression in which specific treatments (e.g., CBT, PDT, IPT, EFT) or no treatment control conditions were directly compared to NDST. By doing so, the authors were able to estimate how much of patient outcomes were attributable to: specific effects of treatments (the difference between a specific intervention and NDST), common effects of treatment (the difference between NDST and no treatment), and extra-therapeutic factors (the effects of no treatment). The meta analysis included 31 studies with over 2500 patients with depression. Twenty-one comparisons included CBT, and the rest included IPT, PDT, or EFT. NDST was significantly less effective than other specific therapies (e.g., CBT, IPT, PDT, or EFT) at post-treatments g = −0.20 (95% CI: −0.32 to −0.08), but the effect was quite small. The difference between NDST and CBT alone (the most researched treatment type) was not statistically significant. Interestingly, when the authors controlled for researcher allegiance (an indication of which treatment was preferred by the researcher), the superior effects of specific treatments over NDST disappeared. NDST was significantly more effective than no-treatment, and the effect was moderate, g=0.58 (95% CI: 0.45–0.72). Pre- to post-treatment change in symptoms in the control condition was statistically significant, g = 0.39 (95% CI: 0.03–0.74), indicating the positive effects of extra-therapeutic factors on depressive symptoms (e.g., events in the patient’s life not related to therapy). Overall, the authors were able to estimate that almost 50% of patient outcomes could be attributed to common factors (therapist interpersonal skills, therapeutic alliance, client expectations, etc.), about 17% was due to specific therapy techniques (cognitive restructuring, two chair techniques, IPT interventions), and about 33% was due to extra-therapeutic factors (e.g., the natural course of depressive symptoms or other events in the patient’s life).
Factors like therapist interpersonal skills and managing the therapeutic relationship appear to account for most (50%) of why patients with depression get better. The specific interventions based on therapy models like CBT account for relatively less of patient outcomes (17%). The natural course of the disorder and other events in patients’ lives account for about a third of patient improvement. Therapists can learn how to maximize the effects of common factor skills through deliberate practice and training to identify and repair alliance ruptures to help their patients get better.
Group Psychotherapy for Eating Disorders
Grenon, R., Schwartze, D., Hammond, N., Ivanova, I., Mcquaid, N., Proulx, G., & Tasca, G. A. (2017). Group psychotherapy for eating disorders: A meta-analysis. International Journal of Eating Disorders. DOI: 10.1002/eat.22744
Group therapy has an evidence base indicating its efficacy for many disorders. Groups represent a social microcosm in which interpersonal factors that underlie psychological distress and symptoms can be effectively addressed. Group therapeutic factors include peer interpersonal feedback, social learning, emotional expression, and group cohesion. Theories of eating disorder symptoms include interpersonal problems and affect dysregulation as maintenance factors. Many treatment guidelines indicate that individual and group CBT are the treatments of choice for eating disorders. However, there are no meta analyses that specifically look at the efficacy of group therapy for eating disorders. In this study, Grenon and colleagues assess if: (a) group psychotherapy for eating disorders is efficacious compared to wait-list controls, (b) group therapy is effective compared to other active treatments (self help, individual therapy, medications), and (c) group CBT is more effective than other types of group therapy (group interpersonal therapy [GIPT], group psychodynamic-interpersonal psychotherapy [GPIP], or group dialectical behavior therapy [GDBT]). The authors reviewed 27 randomized controlled trials with over 1800 patients that provided direct comparisons of group therapy for eating disorders. The mean drop out rate from group therapy was 16.47% (SD = 13.46), which is similar to what is reported for psychotherapy trials in general. Group therapy was significantly more effective than wait list controls in achieving abstinence from binge eating and purging (RR = 5.51, 95% CI: 3.73, 8.12), decreasing the frequency of binge eating and/or purging (g = 0.70, 95% CI: 0.51, 0.90), and reducing related psychopathology (g = 0.49, 95% CI: 0.32, 0.66). Group psychotherapy had an overall rate of abstinence from binge eating of 51.38%, while wait-list control conditions had an overall abstinence rate of 6.51%. Similar findings were achieved a follow-ups. The effects of group psychotherapy and other active treatments (e.g., behavioral weight loss, self-help, individual psychotherapy) did not differ on any outcome at post-treatment or at follow-ups. Group CBT and other forms of group psychotherapy did not differ significantly on outcomes at any time point.
The results add to a growing body of research that indicates that group psychotherapy is as effective as other treatments, including individual therapy, to treat mental disorders. Despite the fact that practice guidelines indicate that CBT is the treatment of choice for eating disorders, this meta analysis did not provide evidence that group CBT was more effective than other types of group treatments. Clinicians considering group interventions for eating disorders or other mental health problems will do well to make use of group therapeutic factors like interpersonal learning, peer feedback, emotional expression, and group cohesion to improve patient outcomes.
Specific and Non-Specific Effects in Psychotherapy
Palpacuer, C., Gallet, L., Drapier, D., Reymann, J-M., Falissard, B., & Naudet, Florian (2016). Specific and non-specific effects of psychotherapeutic interventions for depression: Results from a meta-analysis of 84 studies. Journal of Psychiatric Research.
Specific effect in psychotherapy refer to those technical interventions that are based on a treatment model that are specific to a particular modality. For example, the effects on symptoms caused by transference interpretations, cognitive restructuring, or exposure might all be considered specific effects. Non-specific effects is a very broad term that sometimes refers to effects on symptoms caused by common factors across all psychotherapies like therapist empathy, therapeutic alliance, or positive regard. Non-specific effects has also been used to refer to any extra-therapeutic effects that are more peripherally related to treatments, like type of control groups used in a study, researcher allegiance, number of treatment sessions, or length of follow-up. In this meta-analysis of 84 studies of over 6000 participants, Palpacuer and colleagues examined the association between non-specific factors (defined as intervention format [group or individual], client demographics, number of treatment sessions, length of follow up, and researcher allegiance to one of the treatment modalities) and treatment outcomes for depression. First, they looked at whether the specific type of intervention (cognitive behavioral, psychodynamic, interpersonal, problem solving, and others) was associated with reductions in depressive symptoms. Second, they assessed if the non-specific factors added to the prediction of improved depressive symptoms and accounted for some of the effects of specific types of interventions. Similar to previous findings, all psychotherapies were significantly more effective than waiting-list controls. However, the effects of the specific intervention approaches became non-significant when the non-specific factors were included in the analysis. That is, non-specific factors seemed to account for some of the effects of the specific treatments. In particular, if the study was conducted in North America vs Europe (β = 0.55, 95% CI: 0.22; 0.90), if the researcher had an allegiance to a particular therapeutic approach (β = 0.29, 95% CI: 0.07; 0.52), or if the number of sessions was higher (β = 0.03, 95% CI: 0.01; 0.04) then depressive outcomes were better.
This meta analysis of over 87 studies suggests that although various psychotherapies are effective, there remain questions about how and why they work. For example, the findings suggest that North American patients may have different expectations and higher responses to treatment, that a researcher's belief in the effectiveness of their favored intervention actually improves patients' outcomes, and that a higher number of sessions may also result in better outcomes. These factors appear to account for an important proportion of the specific effects of each type of psychotherapy.
Psychotherapists Matter When Evaluating Treatment Outcomes
Owen, J., Drinane, J. M., Idigo, K. C., & Valentine, J. C. (2015). Psychotherapist effects in meta-analyses: How accurate are treatment effects? Psychotherapy, 52(3), 321-328.
One of the ongoing debates in the psychotherapy research literature has to do with the relative efficacy of psychotherapies. Is psychotherapy brand A (CBT, for example) more effective than psychotherapy brand B (psychodynamic therapy, for example)? The most common way to test this question is with randomized controlled trials (RCTs), in which clients are randomly assigned to treatment condition (brand A or B). This study design controls for systematic bias in the results that may be caused by differences between clients. But what about therapists? We know for example that therapist effects (i.e., differences between therapists) account for approximately 5% to 10% of client outcomes. Therapist effects are often larger than the effect of the empirically supported treatment that is being offered. Yet it is almost unheard of for therapists to be randomized to treatments, so therapist effects are not controlled in most psychotherapy trials. As a result the effects of the differences between therapists get statistically rolled into the treatment effects. As Owen and colleagues point out, the impact of not controlling for therapist effects is that some differences between treatments in an RCT will appear statistically significant when in fact they are not. One can control for the effect of therapist differences, thus providing a more accurate estimate of treatment effects, but this is rarely done in published RCTs. So, when these RCTs are summarized in a meta analysis, the meta analysis results are also affected by ignoring therapist effects. In their study, Owen colleagues did something very clever. They took data from 17 recent meta analyses of RCTs that found differences between two interventions. These included meta analyses of studies comparing: CBT vs alternative treatments, bona fide treatments vs non-bona fide treatments, culturally adapted treatments vs those that were not adapted, etc. There are many other meta analyses that show no differences between treatments, but the authors wanted to focus specifically on the 17 that did show differences. Owen and colleagues statistically estimated what would happen to the original study findings of significant differences between treatments if therapist effects on patient outcomes were controlled. They controlled for three different sizes of therapist effects that accounted for: 5% (small), 10% (medium), or 20% (large) of patient outcomes. Even small therapist effects (5%) reduced the number of significant differences between treatments from 100% to 80%. When psychotherapist effects were estimated to be medium (10% - which is the best estimate based on research), the number of significant differences between treatments dropped to 65%. For large therapist effects (20%), the number of significant treatment differences was only 35%.
I have argued previously that the psychotherapist matters. Placing more time and effort in developing good reflective practice based on quality information and developing therapist skills like empathy, progress monitoring, and identifying and repairing alliance ruptures will result in better patient outcomes. As Owen and colleagues note, when reading an RCT that claims to find significant differences between psychotherapies, ask yourself if they took into account the effects of differences between therapists.
Are The Parts as Good as The Whole?
Bell, E. C., Marcus, D. K., & Goodlad, J. K. (2013). Are the parts as good as the whole? A meta-analysis of component treatment studies. Journal of Consulting and Clinical Psychology, 81, 722-736.
Component studies (i.e., dismantling treatments or adding to existing treatments) may provide a method for identifying whether specific active ingredients in psychotherapy contribute to client outcomes. In a dismantling design, at least one element of the treatment is removed and the full treatment is compared to this dismantled version. In additive designs, an additional component is added to an existing treatment to examine whether the addition improves client outcomes. If the dismantled or added component is an active ingredient, then the condition with fewer components should yield less improvement. Among other things, results from dismantling or additive design studies can help clinicians make decisions about which components of treatments to add or remove with some clients who are not responding. For example, Jacobson and colleagues (1996) conducted a dismantling study of cognitive-behavioral therapy (CBT) for depression. They compared: (1) the full package of CBT, (2) behavioral activation (BA) plus CBT modification of automatic thoughts, and (3) BA alone. This study failed to find differences among the three treatment conditions. These findings were interpreted to indicate that BA was as effective as CBT, and there followed an increased interest in behavioral treatments for depression. However, relying on a single study to influence practice is risky because single studies are often statistically underpowered and their results are not as reliable as the collective body of research. One way to evaluate the collective research is by meta analysis, which allows one to assess an overall effect size in the available literature (see my November, 2013 blog on why clinicians should rely on meta analyses). In their meta analysis, Bell and colleagues (2013) collected 66 component studies from 1980 to 2010. For the dismantling studies, there were no significant differences between the full treatments and the dismantled treatments. For the additive studies, the treatment with the added component yielded a small but significant effect at treatment completion and at follow-up. These effects were only found for the specific problems that were targeted by the treatment. Effects were smaller and non-non-significant for other outcomes such as quality of life.
Psychotherapists are sometimes faced with a decision about whether to supplement current treatments with an added component, or whether to remove a component that may not be helping. Adding components to existing treatments leads to modestly improved outcomes at least with regard to targeted symptoms. Removing components appears not to have an impact on outcomes. The findings of Bell and colleagues’ (2013) meta analysis suggest that specific components or active ingredients of current treatments’ have a significant but small effect on outcomes. Some writers, such as Wampold, have argued that the small effects of specific components highlight the greater importance of common factors in psychotherapy (i.e., therapeutic alliance, client expectations, therapist empathy, etc.). This may be especially the case when it comes to improving a patient’s quality of life.
Author email: email@example.com