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
The Client’s Perspective on Psychotherapy
Timuluk, L. & Keogh, D. (2017). The client’s perspective on (experiences of) psychotherapy: A practice-friendly review. Journal of Clinical Psychology, 73, 1556-1567.
Psychotherapy studies that ask clients for their perspective on the treatment or therapist are surprisingly rare. Researchers have conducted such studies over many decades, but there exist very few of them. This is curious given that respecting clients’ preferences for types of therapy or for therapists’ behaviors is predictive of good mental health outcomes. Giving voice to clients’ perspectives is consistent with the notion that psychotherapy is a co-constructed endeavour rather than something that a therapist does to a client (as is the case for a medical intervention). In this review, Timuluk and Keogh review the research in which patients were interviewed for their perspective on a wide range of aspects of psychotherapy. The research indicates a number of things that clients value, that help, and that hinder their progress in therapy. Clients value a number of therapist traits like friendliness, warmth, respect, offering appropriate guidance, and understanding. This research showed that clients recognize that the relationship (i.e., the alliance) has therapeutic effects. Clients report that many forms of therapist behaviors help to develop a therapeutic alliance including eye contact, smiling, warm personalized greetings, paraphrasing, identifying client feelings, and referring to material from previous sessions. Clients find some events in therapy to be unhelpful or that hinder their progress, like feeling exposed and unprotected, being emotionally overwhelmed, and feeling misunderstood by the therapist.
Although clients do value therapist expertise in applying therapeutic techniques, they hold therapist personal qualities like warmth, authenticity, honesty, and dedication as necessary prerequisites for therapy. Clients view the therapist’s interpersonal manner as key to forming a therapeutic relationship. It is important that therapists are aware of how they feel towards a client (countertransference), and how these feelings might impact the way in which they communicate through body language, tone of voice, and behaviors. Effective therapists are willing to seek their client’s perspectives, and are open and non-defensive about what a client has to say about the therapy or therapist, even if negative. Therapist openness to feedback will inevitably lead to a stronger relationship and collaboration with the client, and to better outcomes for the client.
Drop-out From Using Smart Phone Apps for Depression is High
Torous, J., Lipschitz, J., Ng, M., & Firth, J. (2020). Dropout rates in clinical trials of smartphone apps for depressive symptoms: A systematic review and meta-analysis. Journal of Affective Disorders, 263, 413-419.
Depression is a leading cause of disability worldwide, and yet more than 50% of people do not have access to adequate therapy. One solution might be to provide individuals with smartphone apps to help screen, monitor, or provide treatment. Smart phones are ubiquitous, and depression apps are one of the most downloaded categories of apps by the public. Research seems to suggest that smartphone apps provide some positive results for members of the public, but these findings are compromised by the high drop-out rates reported in the primary studies. Further, one study found that although many people download the apps, only about 4% actually use them. Whereas smartphone apps appear attractive to the consumer, very few actually make use of and therefore benefit from them. In this systematic review, Torous and colleagues conduct a meta-analysis of drop-out rates from studies that test the use of smart phone apps. They found 18 independent studies representing data from 3,336 participants who received a psychological intervention for depression via a cell phone app, or who were in a placebo control condition. A total of 22 different apps were tested in the studies. Initially, the pooled drop-out rate from the depression app treatment arms appeared to be about 26.2% (95% C.I.=11.34% to 46.75%), which would be in line with average drop-out rates from randomized controlled trials of face to face psychotherapy. But, the authors noted two things. First, the drop-out rate from the placebo control conditions (14.2%; 95% C.I. = 8.236 to 23.406) was almost half as high as that found for the apps. Second, through some sophisticated statistical analyses, they found evidence of “publication bias” in this research area. This means that a number of studies testing these apps likely were completed but never published (i.e., these might be studies funded by an app manufacturer that demonstrated negative findings or high drop-out rates). When the authors statistically adjusted for publication bias, they found that the actual drop-out rate from the apps was about 47.8%. That is, almost half of users did not complete or dropped out of the studies. There were no differences in drop-out between types of interventions (CBT, mindfulness, or others), and studies with larger sample sizes (i.e., better quality studies) had higher drop-out rates.
Although smartphone apps appear really attractive and may be potentially useful as an adjunct to face to face psychotherapy for depression, their utility is plagued by extremely low usage rates (4%) and high drop-out rates from studies (almost 50%). Leading writers and researchers define psychotherapy as primarily a healing relationship that also includes specific interventions. The key ingredient is the human relationship. Depressed or otherwise troubled individuals cannot (because of feeling demoralized) or will not interact with a machine for healing. One way or another, when it comes to smartphone apps, depressed individuals are voting with their feet. Given these findings, health care providers should consider the ethics of giving a depressed individual only e-therapy as the primary mode of treatment.
What Does a Good Outcome Mean to Patients?
De Smet, M. M., Meganck, R., De Geest, R., Norman, U. A., Truijens, F., & Desmet, M. (2020). What “good outcome” means to patients: Understanding recovery and improvement in psychotherapy for major depression from a mixed-methods perspective. Journal of Counseling Psychology, 67(1), 25–39.
Many researchers consider the randomized controlled trial (RCT) as the best research design for testing medical and psychological treatments. However, critics of the design point to its limitations. For example, in order to collect homogenous samples of patients, researchers may exclude those with complex comorbidities. As a result, patient samples in RCTs may not represent patients one might see in real clinical practice. Also, researchers, and not patients, tend to define the meaning of what is a “good outcome” in these studies. It is possible that researchers and patients may not share the same definition of what it means to have a good outcome from psychotherapy. One key statistical and measurement method that researchers use to define outcomes is the reliable change index, which calculates the degree of change on a symptom scale from pre-treatment to post-treatment relative to the unreliability of the measurement. Using this method, researchers classify patients as “recovered” (reliably changed and passing a clinical cut-off score), “improved” (reliably changed but remaining in the clinical range), “not improved”, or “deteriorated”. However, this commonly used approach does not indicate whether the changes are actually meaningful to the patients. In this study, De Smet and colleagues interviewed patients from a randomized controlled trial of time-limited psychotherapy (16 sessions of CBT vs psychodynamic therapy) for depression who were classified as “recovered” or “improved” at post-treatment based on the reliable change index of a commonly used depression self-report scale. The authors asked how the patients experienced their depression symptom outcome, and what changes the patients valued since the start of therapy. In the original treatment trial of 100 patients, 28 were categorized as “recovered” and 19 patients were categorized as “improved”. During the post-therapy interview, the “recovered” and “improved” patients typically reported a certain degree of improvement in their symptoms. However, the patients categorized as “improved” reported that their gains were unstable from day to day, some reported having relapsed, and half did not feel that they improved at all. None of the “recovered” patients indicated that they felt “cured” of depression. Patients identified three domains of change that they experienced and valued. First, they felt empowered – that is, they had increased self-confidence, greater independence, and new coping skills. Second, they found a personal balance – that is, they had better relationships with loved ones, felt calmer, and had greater insight into their problems. Third, patients tended to identify ongoing struggles despite positive changes in the other domains – that is, certain key problems remained unresolved. “Improved” patients, and even some in the “recovered” group, indicated that their core difficulties had not been altered by the therapy.
Although measurement of symptom change can give a clinician a general sense of how the patient is doing with regard to their symptoms and whether the patient is on track, such measurement may not capture the complexity of patients’ experiences of the therapy and any broader changes they may value. Patients in this trial, especially those classified as “improved”, had varied experiences. Aside from symptom reduction, clinicians should assess what their patients may value, such as: better relationships, greater self-understanding, more self-confidence, and feeling calmer. Most patients, including some who “recovered”, felt that they were engaged in an ongoing struggle, even after therapy. These findings suggest that addressing some of the core difficulties patients face may require longer term psychotherapy.
What do Patients Want from Psychotherapy?
Cuijpers, P. (2020) Measuring success in the treatment of depression: What is most important to patients? Expert Review of Neurotherapeutics, 20, 123-125.
There is lots of evidence now that psychotherapies of various types are efficacious for the treatment of depression. Psychotherapy trials focus largely on depressive symptoms, and define major depression according to psychiatric diagnostic manuals. However, the diagnosis of major depression, for example, is not a unitary construct. That is, it is simply a collection of symptoms and signs that are purported to make up a category of disorder. In fact, people with major depression are quite varied on a whole range of things, like severity, coping style, motivation, attachment style, personality, and extent of comorbidity with other diagnoses. This means that many psychotherapy studies may be focusing on patient outcomes (i.e., reduction of depressive symptoms) that may or may not be important to patients. In this paper, Cuijpers reviews the literature on what patients want from psychotherapy. He found that while symptom reduction was important to patients with depressive disorders, it was not the only outcome they wanted from psychotherapy. Patients also want to have a more fulfilling lives, to return to productive work, to solve conflicts with close loved ones, to learn to live with a chronic disability or disease, to learn to handle the effects of trauma, and other quality of life issues. Fortunately, some studies do report the effects of psychotherapy on quality of life, social functioning, anxiety, hopelessness, and interpersonal problems. However, even these studies treat such outcomes as if they were uniformly important to all patients in the study. Very few studies take a personalized approach to patient outcomes, in which the outcomes of interest are those determined by each patient specific to their own circumstances and wishes.
Psychotherapists who practice from an evidence-informed perspective often try to measure outcomes in their own practices using reliable measurements. However, many of these measurements may be too general for any specific patient, or they may represent outcomes that do not align with what the individual patient wants. Practicing clinicians who assess outcomes in their own practices, may want to consider supplementing standard symptom outcome measures with more personalized assessments for patients.
A Brave New World of Training and Consultation in Psychotherapy
Imel, Z. E., Pace, B. T., Soma, C. S., Tanana, M., Hirsch, T., Gibson, J., Georgiou, P., Narayanan, S., & Atkins, D. C. (2019). Design feasibility of an automated, machine-learning based feedback system for motivational interviewing. Psychotherapy, 56(2), 318–328.
I do not mean to conjure up the image of a dystopian future, but I could not resist the pithy title for this blog. Ideally, psychotherapists in training or those who seek professional development would receive high quality accurate feedback about their behavior (e.g., about interpersonal skills, empathy, vocal tone, body language) and competence (e.g., regarding specific interventions) in real time. This would allow psychotherapists and trainees can make fine-tuned adjustments to their behaviors and interventions that match or complement the specific patient with which they are working. But, given the current technology, this is impossible. Instead psychotherapy training and feedback to practicing clinicians is slow, cumbersome, and imprecise. Current supervision and consultation practices rely on giving feedback based on the clinician’s verbal case report or, at best, based on viewing video recordings. There are systems that provide feedback on patient outcomes that may alert psychotherapists to something going amiss in for the patient. But such feedback occurs post-session, is based on patient self-report, and does not inform immediate in-session therapist behaviors. In this study, Imel and colleagues evaluated an initial proof of concept of an automated feedback system that generated quality metrics about specific therapist interventions and about therapist skills like empathy. They used computer technology based on natural language processing to take conversational data from video of psychotherapy sessions in order to answer questions like: “what did the therapist and patient talk about during the session?”, “how empathic was the therapist?”, and “how often did the therapist use reflections versus closed questions in the session?” The authors developed a machine learning tool to transcribe, code, and rapidly generate feedback to 21 experienced and novice therapists who recorded a 10-minute session with a standardized patient (a standardized patient is an actor who loosely follows a script). The machine learning technology was accurate at defining or coding a “closed question” by a therapist (e.g., a question with a yes/no answer; inter rater agreement with a human coder ICC = .80), but not as accurate at defining or coding a therapist empathic statement (inter rater agreement with a human coder ICC = .23). The system provided immediate feedback the therapists about their behaviors during the session using graphics and text (fidelity to specific interventions, counseling style, empathy, percent open/closed questions, percent reflections). All therapists rated the tool as “easy to use”, 86% strongly agreed that the feedback was representative of their performance, 90% agreed that if the tool was available, they would use it in their clinical practice.
Typically, professional consultation or supervision involves a consultant giving the therapist feedback based on imprecise descriptions of events in a therapy session that occurred at some point in the recent past. This method of training and consultation in psychotherapy has not changed much in the past 60 years. One key drawback of current methods of training and consultation is that they do not make use of real-time feedback to help therapist adjust behaviors to the specific patient or context. It is possible that in the near future with rapid advances in artificial intelligence and machine learning a therapist will be able to finish a session with a patient and receive an immediate feedback report about the previous hour. The feedback might include metrics on empathy, the percent of questions vs reflections, competence in specific interventions, among other personalize ratings. This future might also have novice trainees receive immediate real-time in-session feedback about behaviors of interest that need to be adjusted, or for which more training is necessary. For some, this might be a vision of a dystopian future, for others it may represent a way forward in which therapists achieve more refined skills and better patient outcomes.
Psychotherapy, Pharmacotherapy, and their Combination for Adult Depression
Cuijpers, P., Noma, H., Karyotaki, E., Vinkers, C.H., Cipriani, A., & Furukawa, T.A. (2020). A network meta‐analysis of the effects of psychotherapies, pharmacotherapies and their combination in the treatment of adult depression. World Psychiatry, 19, 92-107.
Mental disorders represent a significant health burden worldwide, with over 350 million people affected. Depression is the second leading cause of disease burden. There is ample evidence that psychotherapies and pharmacotherapies are effective in the treatment of depression. There is also evidence for the efficacy of different types of psychotherapy (CBT, IPT, PDT), and for different types of antidepressant medications. Some research suggests that combining psychotherapy and medications is better than either intervention alone, but the evidence is inconclusive. Existing meta analyses only compare two existing treatments directly to each other at a time: psychotherapy vs medications, psychotherapy vs combined treatments, medications vs combined treatments. In this meta-analysis, Cuijpers and colleagues use a method called “network meta-analysis” to study the relative impact of medications, psychotherapy, or their combination. Network meta-analysis is controversial because it relies on indirect comparisons to estimate effects. For example, let’s say one study compared medications (A) to psychotherapy (B), and another study compared medication (A) to combination treatment (C), then a network meta-analysis would estimate the effects of psychotherapy vs combination treatment by using the transitive principle (if A = B, and B = C, then A = C). This logic relies on everything being equivalent across studies. However, in treatment trials one cannot assume that the different studies comparing A, B, and C are equivalent in terms of quality and bias (in fact, we know they are not). In any case, Cuijpers and colleagues found that combined treatment was superior to either psychotherapy alone or pharmacotherapy alone in terms of standardized effect sizes (0.30, 95% CI: 0.14-0.45 and 0.33, 95% CI: 0.20-0.47). No significant difference was found between psychotherapy alone and pharmacotherapy alone (0.04, 95% CI: –0.09 to 0.16). Interestingly, acceptability (defined as lower patient drop-out rate and better patient adherence to the treatment) was significantly better for combined treatment compared with pharmacotherapy (RR=1.23, 95% CI:
1.05-1.45), as well as for psychotherapy compared with pharmacotherapy (RR=1.17, 95% CI: 1.02-1.32). In other words, pharmacotherapy alone was less acceptable to patients than another treatment approach that included psychotherapy.
This network meta-analysis by a renowned researcher and in a prestigious journal adds to the controversy around the relative efficacy of psychotherapy vs medications vs their combination. What is clear is that patients find medication alone to be less acceptable as a treatment option, and previous research shows that patients are 4 times more likely to prefer psychotherapy over medications. Unfortunately, most people with depression receive medications without psychotherapy.