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
Association Between Insight and Outcome of Psychotherapy
Jennissen, S., Huber, J., Ehrenthal, J.C., Schauenburg, H., & Dinger, U. (2018). Association between insight and outcome of psychotherapy: Systematic review and meta-analysis. The American Journal of Psychiatry. Published Online: https://doi.org/10.1176/appi.ajp.2018.17080847
For many authors, one of the purported mechanisms of change in psychotherapy is insight. In fact, the utility of insight for clients with mental health problems was first proposed over 120 years ago by Freud and Breuer. Briefly, insight refers to higher levels of self-understanding that might result in fewer negative automatic reactions to stress and other challenges, more positive emotions, and greater flexibility in cognitive and interpersonal functioning. Although insight is a key factor in some psychodynamic models, it also plays a role in other forms of psychotherapy. Experiential psychotherapy emphasises gaining a new perspective through experiencing, and for CBT insight relates to becoming more aware of automatic thoughts. Jennissen and colleagues defined insight as patients understanding: the relationship between past and present experiences, their typical relationship patterns, and the associations between interpersonal challenges, emotional experiences, and psychological symptoms. In this study, Jennissen and colleagues conducted a systematic review and meta analysis of the insight-outcome relationship, that is the relationship between client self-understanding and symptom reduction. They reviewed studies of adults seeking psychological treatment including individual or group therapy. The predictor variable was an empirical measure of insight assessed during treatment but prior to when final outcomes were evaluated. The outcome was some reliable and empirical measure related to symptom improvement, pre- to post- treatment. The review turned up 22 studies that included over 1100 patients mostly with anxiety or depressive disorders who attended a median of 20 sessions of therapy. The overall effect size of the association between insight and outcome was r = 0.31 (95% CI=0.22–0.40, p < 0.05), which represents a medium effect. Moderator analyses found no effect of type of therapy or diagnosis on this mean effect size, though the power of these analyses was low.
The magnitude of the association between insight and outcome is similar to the effects of other therapeutic factors such as the therapeutic alliance. When gaining insight, patients may achieve a greater self-understanding, which allows them to reduce distorted perceptions of themselves, and better integrate unpleasant experiences into their conscious life. Symptoms may be improved by self-understanding because of the greater sense of control and master that it provides, and by the new solutions and adaptive ways of living that become available to clients.
Author email: Simone.Jennissen@med.uni-heidelberg.de
Is Psychotherapy Effective? Revisited.
Munder, T., Fluckiger, C., Leichsenring, F, Abbass, A.A., Hilsenroth, M.J., … Wampold, B.E. (2018). Is psychotherapy effective? A re-analysis of treatments for depression. Epidemiology and Psychiatric Sciences, 1-7.
Based on a deeply flawed review in 1952, Hans Eysenck declared that psychotherapy was no more effective than custodial care for treating mental disorders. Later, he qualified this by stating that behaviour therapy was effective and other forms of psychotherapy were not. These statements touched off decades of angst and debate in the psychotherapy community, and also resulted in a great deal of research about psychotherapy’s effectiveness. By the 1970s the new research technique of meta-analysis was developed and was applied to psychotherapy research. In their seminal meta analysis of controlled studies, Smith and Glass found that psychotherapy was useful and with large effects compared to no treatment. And yet the debate continues. In 2018, Cuijpers argued that waitlist control groups (i.e., a common control condition in psychotherapy studies in which patients receive no treatment) are an inappropriate comparison leading to exaggerated estimates of the effects of psychotherapy. Recently, Munder and colleagues argued that waitlist controls are a way of estimating the natural course of the disorder (what would happen with no treatment) plus the effect of expecting to receive treatment (client expectations of receiving treatment tend to have a positive impact on symptoms). In fact, research shows that pre- to post-study effect sizes for the waiting period is approximately g = .40, or a medium effect. In other words, waiting for therapy in a study results in a moderate proportion of individuals getting better on their own without treatment. Therefore, Munder and colleagues argued that comparing psychotherapy to a waitlist control is appropriate and may be a conservative estimate of psychotherapy’s effects (i.e., psychotherapy has to outperform the effects of clients expecting treatment to help them). In their meta analysis, Munder and colleagues re-analysed 71 studies of psychotherapy for depression compared to a waitlist control condition. They found that the effect size in favour of psychotherapy was g = 0.75 (SE = 0.09) indicating a moderate to large effect. Psychotherapy was also more effective than care as usual (i.e., compared to another intervention that was not psychotherapy), g = 0.31 (SE = 0.11). There were no differences between types of psychotherapy (CBT, IPT, PDT, etc.) for depression outcomes.
Despite various attempts during the history of psychotherapy to downplay or disparage its efficacy, research continues to show that psychotherapy is in fact effective. The average effect size compared to the natural history of depression is moderate to large (and that is likely an under-estimate). Again, there is no evidence that one type of psychotherapy is superior to another for treating depression. It is time for the field to move beyond questions of efficacy of psychotherapy and of the relative efficacy of different treatments, and look to understanding therapist interpersonal stances, client characteristics, and relationship factors that may improve outcomes from psychotherapy.
How Reliable is the Association Between Therapeutic Alliance and Patient Outcomes?
Flückiger, C., Del Re, A. C., Wampold, B. E., & Horvath, A. O. (2018). The alliance in adult psychotherapy: A meta-analytic synthesis. Psychotherapy. Advance online publication. http://dx.doi.org/10.1037/pst0000172
The therapeutic alliance is one of the most researched concepts in psychotherapy. The alliance, also called the working alliance or therapeutic alliance, consists of the collaborative agreement between patient and therapist on the tasks (what to do) and goals (what to achieve) of their therapeutic work together. Alliance also includes the relational or emotional bond between therapist and patient. It is different from therapist empathy, transference, countertransference, the real relationship and other concepts related to the therapeutic relationship. Researchers and clinicians have known for years about the importance of developing and maintaining an alliance to achieving patient outcomes. The growing research in this area now allows one to see how stable this finding is. Fluckiger and colleagues conducted a meta analysis of 306 studies with over 30,000 patients that assessed the alliance-outcome relationship. The research occurred in naturalistic settings (during regular clinical practice) and in randomized controlled trials. The overall effect size based on 295 independent comparisons was r = .278 (95% CI: .256, .299), indicating a statistically significant medium-sized association accounting for about 8% of treatment outcomes. To put this in perspective, this effect is as large as or larger than the effects of many common medical interventions. The type of therapy made no difference to this finding - the alliance was just as important to CBT as it was to psychodynamic, interpersonal, and emotionally focused therapies. The alliance-outcome correlation was somewhat smaller, though still significant among those with substance-use disorders, but otherwise was consistent for all other disorders tested (depression, anxiety, PTSD, borderline personality disorder). The alliance measure used, who rated the alliance, when it was assessed, and the outcome that was measured tended to have a small or no impact on the results. The alliance-outcome relationship was just as important to everyday clinical practice as it was in randomized controlled trials.
The alliance-outcome association is highly reliable or stable across a number of therapies, diagnoses, measurements, and study designs. This very large body of research suggests that therapists should: (1) build and maintain an emotional bond, and agreement on tasks and goals with patients throughout therapy; (2) develop the alliance early by focusing on agreement on treatment and goals; (3) address ruptures in the alliance early and immediately; and (4) assess the strength and quality of the alliance regularly throughout treatment from the patient’s perspective using a well-known brief alliance measure.
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.
Placebo Response in Transcranial Magnetic Stimulation for Depression
Razza, L. B., Moffa, A. H., Moreno, M. L., Carvalho, A. F., Padberg, F., Fregni, F., & Brunoni, A. R. (2018). A systematic review and meta-analysis on placebo response to repetitive transcranial magnetic stimulation for depression trials. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 81, 105-113.
Transcranial magnetic stimulation (TMS) is a new treatment for depression thought to modulate brain activity through electromagnetic pulses delivered by a coil placed over the patient’s scalp. A meta analysis shows that TMS may be effective in treating depressive disorders when compared to a placebo control, although only 18.6% of those receiving TMS were no longer depressed at the end of treatment. The placebo control condition usually involves a sham version of TMS in which the coil is placed over the scalp but no magnetic stimulation is applied. In antidepressant trials, the placebo response is quite high such that approximately 40% of patients respond to the placebo condition (in antidepressant trials, the placebo condition includes an identical pill that is inert). In this meta analysis, Razza and colleagues assess the placebo response in TMS. They included only double blind randomized controlled trials (i.e., trials in which both the patient and physician were not aware if the treatment was real or a sham). The authors estimated the placebo response based on pre- to post-sham TMS scores of common measures of depression. The meta analysis included 61 studies of over 1300 patients. The main result showed that sham response was large (g = 0.80; 95%CI = 0.65–0.95). Trials including patients with only one episode of depression or who were not treatment resistant (g =0.67, 95%CI = 0.06–1.28, p= 0.03) had higher placebo responses than those trials in which patients previously had two or more failed antidepressant treatments (g = 0.5, 95%CI = 0.03–0.99, p = 0.048).
The results of this meta analysis demonstrates a high placebo response in trials testing TMS. This is similar to the high level of placebo response commonly seen in patients in antidepressant medication trials. It appears that psychological factors like attention, instillation of hope, patient expectations of receiving benefit, and perhaps working alliance may account for an important portion of why pharmacological and other medical interventions appear to work for those with depressive disorders. This is particularly true for patients who are receiving treatment for the first time or for whom previous medical treatment was successful.
Are E-Health Interventions Useful for Weight Loss?
Podina, I. R., & Fodor, L. A. (2018). Critical review and meta-analysis of multicomponent behavioral e-health interventions for weight loss. Health Psychology, 37(6), 501-515.
Over 35% of Americans are overweight or obese, and this poses significant health-related challenges. Obesity likely contributes to heart disease, Type II diabetes, and some forms of cancer. Also, obesity is often co-morbid with mental health conditions including depression and binge-eating disorder. Practice guidelines list multicomponent behavioural interventions as state of the art treatment for weight loss. These include dietary counselling, increased physical activity, and behavioural methods to support behaviour change. However, such interventions often require direct in-person contact with a health or mental health professional, which can be expensive and create a barrier to accessing treatment for some. An option to increase access is to deliver the multicomponent behavioural intervention by internet or by another electronic format such as DVD. In this meta analysis, Podina and Fodor reviewed 47 randomized controlled studies representing over 1500 participants in which e-health interventions for weight loss in overweight or obese individuals were tested against in-person treatment or a control condition (no treatment or treatment as usual). E-health interventions were more effective than control conditions for weight loss outcomes at post-treatment, g = 0.34 (95% CI [0.24 to 0.44]). Similar results were found at follow-up. However, e-health interventions were significantly less effective than active in-person treatments, g = -0.31 (95% CI [-0.43 to -0.20]) for weight loss in overweight or obese individuals.
E-health interventions (mostly internet delivered treatment) of multicomponent behavioral treatment for weight loss was more effective than no treatment or treatment as usual. However, e-health was significantly less effective than traditional face to face behavioral interventions to help people reduce their body weight. The authors raised concerns about the use of e-health interventions for weight loss as the first line treatment as the effects were small and the approach was less effective than in-person interventions.