Facilitating dyadic synchrony in psychotherapy sessions: Systematic review and meta-analysis.

Objective: This paper highlights the facilitation of dyadic synchrony as a core psychotherapist skill that occurs at the non-verbal level and underlies many other therapeutic methods. We define dyadic synchrony, differentiate it from similar constructs, and provide an excerpt illustrating dyadic synchrony in a psychotherapy session. Method: We then present a systematic review of 17 studies that have examined the associations between dyadic synchrony and psychotherapy outcomes. We also conduct a meta-analysis of 8 studies that examined whether there is more synchrony between clients and therapists than would be expected by chance. Results: Weighted box score analysis revealed that the overall association of synchrony and proximal as well as distal outcomes was neutral to mildly positive. The results of the meta-analysis indicated that real client-therapist dyad pairs exhibited synchronized behavioral patterns to a much greater extent than a sample of randomly paired people who did not actually speak. Conclusion: Our discussion revolves around how synchrony can be facilitated in a beneficial way, as well as situations in which it may not be beneficial. We conclude with training implications and therapeutic practices.

The Effectiveness of an Intensive Inpatient Psychotherapy Program for Chronic Depression: A naturalistic comparison with wait list.

BACKGROUND: Patients with chronic depression (CD) typically have an early symptom onset, more psychiatric comorbidities, more treatment attempts, and more frequent and longer inpatient hospitalizations than patients with major depressive disorders. The main purpose of this study was to investigate the effectiveness of an intensive inpatient psychotherapy program for patients with chronic depression (CD). The primary research question was whether two intensive psychodynamic inpatient treatments, affect phobia therapy (APT) and VITA, were superior to an outpatient wait list condition, receiving treatment as usual (TAU), at completion of treatment. To investigate if a potential difference between the intensive treatment and the wait list control group was dependent on a specific psychotherapeutic model, the study contrasted two therapies with similar intensity, but different theoretical rationales. METHODS: Two hundred eighty patients with CD were included in a naturalistic study. Patients were assessed at four time points; assessment, start of therapy, end of therapy and 1-year follow-up. Three comparisons were performed with patients matched across groups; Intensive inpatient treatment program (APT + VITA) vs wait list during treatment, APT vs VITA during treatment and APT vs VITA during follow-up. The outcome measure was the BDI-II. RESULTS: Intensive inpatient treatment program vs. wait list showed a significant difference in favor of the intensive treatment. No significant differences were found between APT and VITA during therapy or follow-up; but both groups had large effect sizes during treatment, which were maintained during follow-up. CONCLUSIONS: The intensive inpatient psychotherapy program showed superior effect on chronic depression over an outpatient wait list condition receiving treatment as usual (TAU), but no significant differences were found between the two intensive inpatient psychodynamic treatments. The results provide support for the effectiveness of an intensive inpatient psychotherapy program in treatment of chronic and severe disorders, such as CD, which could be of benefit for policymakers and the health care sector as they are allocating recourses efficiently. TRIAL REGISTRATION: This study has been retrospectively registered on ClinicalTrials.gov (NCT05221567) on February 3rd, 2022.

How do you feel? Using natural language processing to automatically rate emotion in psychotherapy.

Emotional distress is a common reason for seeking psychotherapy, and sharing emotional material is central to the process of psychotherapy. However, systematic research examining patterns of emotional exchange that occur during psychotherapy sessions is often limited in scale. Traditional methods for identifying emotion in psychotherapy rely on labor-intensive observer ratings, client or therapist ratings obtained before or after sessions, or involve manually extracting ratings of emotion from session transcripts using dictionaries of positive and negative words that do not take the context of a sentence into account. However, recent advances in technology in the area of machine learning algorithms, in particular natural language processing, have made it possible for mental health researchers to identify sentiment, or emotion, in therapist-client interactions on a large scale that would be unattainable with more traditional methods. As an attempt to extend prior findings from Tanana et al. (2016), we compared their previous sentiment model with a common dictionary-based psychotherapy model, LIWC, and a new NLP model, BERT. We used the human ratings from a database of 97,497 utterances from psychotherapy to train the BERT model. Our findings revealed that the unigram sentiment model (kappa = 0.31) outperformed LIWC (kappa = 0.25), and ultimately BERT outperformed both models (kappa = 0.48).

Coregulation of therapist and client emotion during psychotherapy.

OBJECTIVE: Close interpersonal relationships are fundamental to emotion regulation. Clinical theory suggests that one role of therapists in psychotherapy is to help clients regulate emotions, however, if and how clients and therapists serve to regulate each other's emotions has not been empirically tested. Emotion coregulation - the bidirectional emotional linkage of two people that promotes emotional stability - is a specific, temporal process that provides a framework for testing the way in which therapists' and clients' emotions may be related on a moment to moment basis in clinically relevant ways. METHOD: Utilizing 227 audio recordings from a relationally oriented treatment (Motivational Interviewing), we estimated continuous values of vocally encoded emotional arousal via mean fundamental frequency. We used dynamic systems models to examine emotional coregulation, and tested the hypothesis that each individual's emotional arousal would be significantly associated with fluctuations in the other's emotional state over the course of a psychotherapy session. RESULTS: Results indicated that when clients became more emotionally labile over the course of the session, therapists became less so. When changes in therapist arousal increased, the client's tendency to become more aroused during session slowed. Alternatively, when changes in client arousal increased, the therapist's tendency to become less aroused slowed.

Development and Evaluation of ClientBot: Patient-Like Conversational Agent to Train Basic Counseling Skills.

BACKGROUND: Training therapists is both expensive and time-consuming. Degree-based training can require tens of thousands of dollars and hundreds of hours of expert instruction. Counseling skills practice often involves role-plays, standardized patients, or practice with real clients. Performance-based feedback is critical for skill development and expertise, but trainee therapists often receive minimal and subjective feedback, which is distal to their skill practice. OBJECTIVE: In this study, we developed and evaluated a patient-like neural conversational agent, which provides real-time feedback to trainees via chat-based interaction. METHODS: The text-based conversational agent was trained on an archive of 2354 psychotherapy transcripts and provided specific feedback on the use of basic interviewing and counseling skills (ie, open questions and reflections-summary statements of what a client has said). A total of 151 nontherapists were randomized to either (1) immediate feedback on their use of open questions and reflections during practice session with ClientBot or (2) initial education and encouragement on the skills. RESULTS: Participants in the ClientBot condition used 91% (21.4/11.2) more reflections during practice with feedback (P<.001) and 76% (14.1/8) more reflections after feedback was removed (P<.001) relative to the control group. The treatment group used more open questions during training but not after feedback was removed, suggesting that certain skills may not improve with performance-based feedback. Finally, after feedback was removed, the ClientBot group used 31% (32.5/24.7) more listening skills overall (P<.001). CONCLUSIONS: This proof-of-concept study demonstrates that practice and feedback can improve trainee use of basic counseling skills.

Automating the assessment of multicultural orientation through machine learning and natural language processing.

Recent scholarship has highlighted the value of therapists adopting a multicultural orientation (MCO) within psychotherapy. A newly developed performance-based measure of MCO capacities exists (MCO-performance task [MCO-PT]) in which therapists respond to video-based vignettes of clients sharing culturally relevant information in therapy. The MCO-PT provides scores related to the three aspects of MCO: cultural humility (i.e., adoption of a nonsuperior and other-oriented stance toward clients), cultural opportunities (i.e., seizing or making moments in session to ask about clients' cultural identities), and cultural comfort (i.e., therapists' comfort in cultural conversations). Although a promising measure, the MCO-PT relies on labor-intensive human coding. The present study evaluated the ability to automate the scoring of the MCO-PT transcripts using modern machine learning and natural language processing methods. We included a sample of 100 participants (n = 613 MCO-PT responses). Results indicated that machine learning models were able to achieve near-human reliability on the average across all domains (Spearman's ρ = .75, p < .0001) and opportunity (ρ = .81, p < .0001). Performance was less robust for cultural humility (ρ = .46, p < .001) and was poorest for cultural comfort (ρ = .41, p < .001). This suggests that we may be on the cusp of being able to develop machine learning-based training paradigms that could allow therapists opportunities for feedback and deliberate practice of some key therapist behaviors, including aspects of MCO. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Improving the quality of counseling and clinical supervision in opioid treatment programs: how can technology help?

BACKGROUND: The opioid epidemic has resulted in expanded substance use treatment services and strained the clinical workforce serving people with opioid use disorder. Focusing on evidence-based counseling practices like motivational interviewing may be of interest to counselors and their supervisors, but time-intensive adherence tasks like recording and feedback are aspirational in busy community-based opioid treatment programs. The need to improve and systematize clinical training and supervision might be addressed by the growing field of machine learning and natural language-based technology, which can promote counseling skill via self- and supervisor-monitoring of counseling session recordings. METHODS: Counselors in an opioid treatment program were provided with an opportunity to use an artificial intelligence based, HIPAA compliant recording and supervision platform (Lyssn.io) to record counseling sessions. We then conducted four focus groups-two with counselors and two with supervisors-to understand the integration of technology with practice and supervision. Questions centered on the acceptability of the clinical supervision software and its potential in an OTP setting; we conducted a thematic coding of the responses. RESULTS: The clinical supervision software was experienced by counselors and clinical supervisors as beneficial to counselor training, professional development, and clinical supervision. Focus group participants reported that the clinical supervision software could help counselors learn and improve motivational interviewing skills. Counselors said that using the technology highlights the value of counseling encounters (versus paperwork). Clinical supervisors noted that the clinical supervision software could help meet national clinical supervision guidelines and local requirements. Counselors and clinical supervisors alike talked about some of the potential challenges of requiring session recording. CONCLUSIONS: Implementing evidence-based counseling practices can help the population served in OTPs; another benefit of focusing on clinical skills is to emphasize and hold up counselors' roles as worthy. Machine learning technology can have a positive impact on clinical practices among counselors and clinical supervisors in opioid treatment programs, settings whose clinical workforce continues to be challenged by the opioid epidemic. Using technology to focus on clinical skill building may enhance counselors' and clinical supervisors' overall experiences in their places of work.

Mental Health Counseling From Conversational Content With Transformer-Based Machine Learning.

Importance: Use of asynchronous text-based counseling is rapidly growing as an easy-to-access approach to behavioral health care. Similar to in-person treatment, it is challenging to reliably assess as measures of process and content do not scale. Objective: To use machine learning to evaluate clinical content and client-reported outcomes in a large sample of text-based counseling episodes of care. Design, Setting, and Participants: In this quality improvement study, participants received text-based counseling between 2014 and 2019; data analysis was conducted from September 22, 2022, to November 28, 2023. The deidentified content of messages was retained as a part of ongoing quality assurance. Treatment was asynchronous text-based counseling via an online and mobile therapy app (Talkspace). Therapists were licensed to provide mental health treatment and were either independent contractors or employees of the product company. Participants were self-referred via online sign-up and received services via their insurance or self-pay and were assigned a diagnosis from their health care professional. Exposure: All clients received counseling services from a licensed mental health clinician. Main Outcomes and Measures: The primary outcomes were client engagement in counseling (number of weeks), treatment satisfaction, and changes in client symptoms, measured via the 8-item version of Patient Health Questionnaire (PHQ-8). A previously trained, transformer-based, deep learning model automatically categorized messages into types of therapist interventions and summaries of clinical content. Results: The total sample included 166 644 clients treated by 4973 therapists (20 600 274 messages). Participating clients were predominantly female (75.23%), aged 26 to 35 years (55.4%), single (37.88%), earned a bachelor's degree (59.13%), and were White (61.8%). There was substantial variability in intervention use and treatment content across therapists. A series of mixed-effects regressions indicated that collectively, interventions and clinical content were associated with key outcomes: engagement (multiple R = 0.43), satisfaction (multiple R = 0.46), and change in PHQ-8 score (multiple R = 0.13). Conclusions and Relevance: This quality improvement study found associations between therapist interventions, clinical content, and client-reported outcomes. Consistent with traditional forms of counseling, higher amounts of supportive counseling were associated with improved outcomes. These findings suggest that machine learning-based evaluations of content may increase the scale and specificity of psychotherapy research.

It's not what you said, it's how you said it: An analysis of therapist vocal features during psychotherapy.

Psychotherapy is a conversation, whereby, at its foundation, many interventions are derived from the therapist talking. Research suggests that the voice can convey a variety of emotional and social information, and individuals may change their voice based on the context and content of the conversation (e.g., talking to a baby or delivering difficult news to patients with cancer). As such, therapists may adjust aspects of their voice throughout a therapy session depending on if they are beginning a therapy session and checking in with a client, conducting more therapeutic "work," or ending the session. In this study, we modeled three vocal features-pitch, energy, and rate-with linear and quadratic multilevel models to understand how therapists' vocal features change throughout a therapy session. We hypothesized that all three vocal features would be best fit with a quadratic function - starting high and more congruent with a conversational voice, decreasing during the middle portions of therapy where more therapeutic interventions were being administered, and increasing again at the end of the session. Results indicated a quadratic model for all three vocal features was superior in fitting the data, as compared to a linear model, suggesting that therapists begin and end therapy using a different style of voice than in the middle of a session.

The Silent Treatment?: Changes in patient emotional expression after silence.

Psychotherapy can be an emotionally laden conversation, where both verbal and non-verbal interventions may impact the therapeutic process. Prior research has postulated mixed results in how clients emotionally react following a silence after the therapist is finished talking, potentially due to studying a limited range of silences with primarily qualitative and self-report methodologies. A quantitative exploration may illuminate new findings. Utilizing research and automatic data processing from the field of linguistics, we analysed the full range of silence lengths (0.2 to 24.01 seconds), and measures of emotional expression - vocally encoded arousal and emotional valence from the works spoken - of 84 audio recordings Motivational Interviewing sessions. We hypothesized that both the level and the variance of client emotional expression would change as a function of silence length, however, due to the mixed results in the literature the direction of emotional change was unclear. We conducted a multilevel linear regression to examine how the level of client emotional expression changed across silence length, and an ANOVA to examine the variability of client emotional expression across silence lengths. Results indicated in both analyses that as silence length increased, emotional expression largely remained the same. Broadly, we demonstrated a weak connection between silence length and emotional expression, indicating no persuasive evidence that silence leads to client emotional processing and expression.

A latent trajectory analysis of inpatient depression treatment.

Patients seeking psychotherapy may progress through treatment in varying ways. Modeling multiple treatment trajectories through growth mixture modeling provides a comprehensive way of understanding a patient population. Multiple trajectories may additionally help researchers describe complexities within a patient population, such as those with severe and persistent disorders and comorbid symptoms, to understand characteristics of patients that may be struggling during treatment. We analyzed the depression symptom outcome measures (PHQ-9) for 246 patients receiving inpatient depression treatment. We constructed a growth mixture model of depression symptom changes, allowing the number of treatment trajectories to emerge through the data, and utilized goodness-of-fit indices to select the superior model. Results indicated three classes was the best fitting model, with patients either (a) patients started above the clinical cutoff score for depression and had significant linear change over time, ending therapy just above the clinical cutoff-"Improvement-leveling off-improvement"; (b) patients started therapy well above the clinical cutoff, showed symptom alleviation at the beginning of therapy before the trajectory started to level off-"High symptom pressure"; or (c) patients started therapy just below the clinical cutoff, had steady change throughout therapy, ending well below the clinical cutoff-"continuous improvement." Implications of the study may include altering the length of treatment based on patient presenting symptoms in order to best serve patients and utilize hospital resources. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Design feasibility of an automated, machine-learning based feedback system for motivational interviewing.

Direct observation of psychotherapy and providing performance-based feedback is the gold-standard approach for training psychotherapists. At present, this requires experts and training human coding teams, which is slow, expensive, and labor intensive. Machine learning and speech signal processing technologies provide a way to scale up feedback in psychotherapy. We evaluated an initial proof of concept automated feedback system that generates motivational interviewing quality metrics and provides easy access to other session data (e.g., transcripts). The system automatically provides a report of session-level metrics (e.g., therapist empathy) and therapist behavior codes at the talk-turn level (e.g., reflections). We assessed usability, therapist satisfaction, perceived accuracy, and intentions to adopt. A sample of 21 novice (n = 10) or experienced (n = 11) therapists each completed a 10-min session with a standardized patient. The system received the audio from the session as input and then automatically generated feedback that therapists accessed via a web portal. All participants found the system easy to use and were satisfied with their feedback, 83% found the feedback consistent with their own perceptions of their clinical performance, and 90% reported they were likely to use the feedback in their practice. We discuss the implications of applying new technologies to evaluation of psychotherapy. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

A multivariate meta-analysis of motivational interviewing process and outcome.

Motivational interviewing (MI) theory proposes a process whereby a set of therapist behaviors has direct effects on client outcomes and indirect effects through in-session processes (e.g., client change talk). Despite clear empirical support for the efficacy of MI across settings, the results of studies evaluating proposed links between MI process and outcome have been less clear. In the present study, we used a series of multivariate meta-analyses to test whether there are differential relationships between specific MI-consistent and MI-inconsistent therapist behaviors, MI therapist global ratings, client change language, and clinical outcomes. Based on 19 primary studies (N = 2,614), we found a significant relationship between MI-consistent therapist behaviors and greater client change talk, as well as greater client sustain talk. Higher therapist global ratings (empathy and MI spirit) were significantly related to increased MI-consistent behaviors, decreased MI-inconsistent behaviors, increased client change talk, yet also increased client sustain talk. Therapist global ratings were not significantly related to clinical outcomes. Client sustain talk was a significant predictor of worse clinical outcomes, while client change talk was unrelated to outcome. Variability within the correlations indicated that MI-consistent and MI-inconsistent therapist behaviors were differentially related to therapist global ratings of empathy and MI spirit. Similar to past research, present findings provide equivocal support for hypothesized MI process outcome relationships. Clinical implications and future areas of MI mechanism research are discussed. (PsycINFO Database Record

The importance of problem-focused treatments: A meta-analysis of anxiety treatments.

One explanation for differences in treatment effectiveness for targeted symptoms is that more-effective treatments are more focused on patients' problems than are less-effective treatments. This conjecture was examined meta-analytically. Comparisons of two treatments of adults with anxiety disorders were included. Effect sizes for targeted symptoms, nontargeted symptoms, and global outcomes (e.g., quality of life and well-being) as well as the relative focus on patients' problems and researcher allegiance were coded. Metaregressions were conducted to predict effect sizes from (a) variables related to the focus on patients' problems and (b) researcher allegiance. For symptom measures, the relative focus on patients' problems predicted the relative effectiveness of the treatments, with the expectations created by explanation appearing more predictive than specific therapeutic actions focused on patients' problems, although conclusions about relative importance were difficult to determine given collinearity of predictors. Researcher allegiance also predicted the effects of the comparisons. For global outcomes, both the focus on patients' problems and researcher allegiance seemed to have smaller roles. A focus on patients' problems appears to be important for the reductions of symptoms. Clinical trials comparing treatments need to balance the focus on patients' problems and reduce researcher allegiance. (PsycINFO Database Record