The long shadow of early-change patterns: a 3-year follow-up after the use of a web-based intervention for mild to moderate depressive symptoms.

Web-based interventions can be effective in treating depressive symptoms. Patients with risk not responding to treatment have been identified by early change patterns. This study aims to examine whether early changes are superior to baseline parameters in predicting long-term outcome. In a randomized clinical trial with 409 individuals experiencing mild to moderate depressive symptoms using the web-based intervention deprexis, three latent classes were identified (early response after registration, early response after screening and early deterioration) based on early change in the first four weeks of the intervention. Baseline variables and these classes were included in a Stepwise Cox Proportional Hazard Multiple Regression to identify predictors associated with the onset of remission over 36-months. Early change class was a significant predictor of remission over 36 months. Compared to early deterioration after screening, both early response after registration and after screening were associated with a higher likelihood of remission. In sensitivity and secondary analyses, only change class consistently emerged as a predictor of long-term outcome. Early improvement in depression symptoms predicted long-term outcome and those showing early improvement had a higher likelihood of long-term remission. These findings suggest that early changes might be a robust predictor for long-term outcome beyond baseline parameters.

Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy.

OBJECTIVE: Given the importance of emotions in psychotherapy, valid measures are essential for research and practice. As emotions are expressed at different levels, multimodal measurements are needed for a nuanced assessment. Natural Language Processing (NLP) could augment the measurement of emotions. The study explores the validity of sentiment analysis in psychotherapy transcripts. METHOD: We used a transformer-based NLP algorithm to analyze sentiments in 85 transcripts from 35 patients. Construct and criterion validity were evaluated using self- and therapist reports and process and outcome measures via correlational, multitrait-multimethod, and multilevel analyses. RESULTS: The results provide indications in support of the sentiments' validity. For example, sentiments were significantly related to self- and therapist reports of emotions in the same session. Sentiments correlated significantly with in-session processes (e.g., coping experiences), and an increase in positive sentiments throughout therapy predicted better outcomes after treatment termination. DISCUSSION: Sentiment analysis could serve as a valid approach to assessing the emotional tone of psychotherapy sessions and may contribute to the multimodal measurement of emotions. Future research could combine sentiment analysis with automatic emotion recognition in facial expressions and vocal cues via the Nonverbal Behavior Analyzer (NOVA). Limitations (e.g., exploratory study with numerous tests) and opportunities are discussed.

Symptom Reduction and Engagement in a Cognitive-Behavioral Mobile Phone App: A Study of User Profiling to Determine Prognostic Indicators.

OBJECTIVE: We investigated the presence of latent transition profiles in a sample of users of a cognitive-behavioral mental health app for the general population. Users' baseline characteristics were used as predictors of the profiles. The role of engagement with the app in the transition profiles was examined. METHOD: A total of 541 users completed the Patient Health Questionnaire-9 and the General Anxiety Disorder-7 when started using the app and 30 days after. Random-Intercept Latent Transition Analysis was implemented to identify users' profiles and transition patterns as classes. The age of the users and the Emotional Self-Awareness Scale-Revised (ESAS-R) were used as predictors of class membership at baseline. The Homework Rating Scale-Mobile Application (HRS-MA; as a measure of engagement) was used as a predictor of class membership at 30 days of app use. RESULTS: A 3-class solution was obtained according to the severity of symptoms (from mild to moderately severe). Age and ESAS-R predicted class membership initially; the higher the age and ESAS-R, the higher the probability of starting using the app with lower distress levels. The HRS-MA predicted class membership at 30 days of app use; the higher the engagement for more symptomatic and younger users, the higher the probability of improvement. However, older users tended to engage less. CONCLUSION: Our findings underpin the relevance of easily accessible digital interventions for young adults with mild to moderate mental health problems. Further studies and developments are required to enhance these apps for older cohorts.

Precision Mental Health and Data-Informed Decision Support in Psychological Therapy: An Example.

Outcome measurement including data-informed decision support for therapists in psychological therapy has developed impressively over the past two decades. New technological developments such as computerized data assessment, and feedback tools have facilitated advanced implementation in several seetings. Recent developments try to improve the clinical decision-making process by connecting clinical practice better with empirical data. For example, psychometric data can be used by clinicians to personalize the selection of therapeutic programs, strategies or modules and to monitor a patient's response to therapy in real time. Furthermore, clinical support tools can be used to improve the treatment for patients at risk for a negative outcome. Therefore, measurement-based care can be seen as an important and integral part of clinical competence, practice, and training. This is comparable to many other areas in the healthcare system, where continuous monitoring of health indicators is common in day-to-day clinical practice (e.g., fever, blood pressure). In this paper, we present the basic concepts of a data-informed decision support system for tailoring individual psychological interventions to specific patient needs, and discuss the implications for implementing this form of precision mental health in clinical practice.

Applying precision methods to treatment selection for moderate/severe depression in person-centered experiential therapy or cognitive behavioral therapy.

OBJECTIVE: To develop two prediction algorithms recommending person-centered experiential therapy (PCET) or cognitive-behavioral therapy (CBT) for patients with depression: (1) a full data model using multiple trial-based and routine variables, and (2) a routine data model using only variables available in the English NHS Talking Therapies program. METHOD: Data was used from the PRaCTICED trial comparing PCET vs. CBT for 255 patients meeting a diagnosis of moderate or severe depression. Separate full and routine data models were derived and the latter tested in an external data sample. RESULTS: The full data model provided the better prediction, yielding a significant difference in outcome between patients receiving their optimal vs. non-optimal treatment at 6- (Cohen's d = .65 [.40, .91]) and 12 months (d = .85 [.59, 1.10]) post-randomization. The routine data model performed similarly in the training and test samples with non-significant effect sizes, d = .19 [-.05, .44] and d = .21 [-.00, .43], respectively. For patients with the strongest treatment matching (d ≥ 0.3), the resulting effect size was significant, d = .38 [.11, 64]. CONCLUSION: A treatment selection algorithm might be used to recommend PCET or CBT. Although the overall effects were small, targeted matching yielded somewhat larger effects.

Process-Based psychotherapy personalization: considering causality with continuous-time dynamic modeling.

Psychotherapy can be improved by integrating the study of mediators (how it works) and moderators (for whom it works). To demonstrate this integration, we studied the relationship between resource activation, problem-coping experiences and symptoms in cognitive-behavior therapy (CBT) for depression, to obtain preliminary insights on causal inference (which process leads to symptom improvement?) and prediction (which one for whom?).A sample of 715 patients with depression who received CBT was analyzed. Hierarchical Bayesian continuous time dynamic modeling was used to study the temporal dynamics between the variables analyzed within the first ten sessions. Depression and self-efficacy at baseline were examined as predictors of these dynamics.There were significant cross-effects between the processes studied. Under typical assumptions, resource activation had a significant effect on symptom improvement. Problem-coping experience had a significant effect on resource activation. Depression and self-efficacy moderated these effects. However, when system noise was considered, these effects may be affected by other processes.Resource activation was strongly associated with symptom improvement. To the extent of inferring causality, for patients with mild-moderate depression and high self-efficacy, promoting resource activation can be recommended. For patients with severe depression and low self-efficacy, promoting problem-coping experiences can be recommended.

Is it me, is it you or is it both of US? Applying the social relations model to disentangle the components of the therapeutic bond.

OBJECTIVE: The study investigated the contribution of therapists and patients to the therapeutic bond and their associations (at the within and between levels) to treatment outcome. On this aim, the social relations model (SRM, aimed to analyze dyadic interpersonal data) was implemented. METHOD: A novel design for individual psychotherapy studies was adopted, a many-with-many asymmetrical block dyadic design, in which several patients interact with several therapists. Hierarchical linear models were computed to study through variance partitioning the different components of the SRM and their association to treatment outcome. RESULTS: All SRM components (with significant effects at therapist- and patient- within and between levels) resulted in significant contributions to the bond. However, only components at the within- and between-therapist, and within-patient levels resulted in significant associations with outcome. CONCLUSION: Given the dyadic nature of the bond, our results support not only studying and offering clinical training on interpersonal therapeutic skills but also on constant monitoring and feedback of the relationship at the more idiosyncratic level.

Adaptation of the Spanish Grief and Meaning Reconstruction Inventory: An Initial Validation and Network Analysis.

The current study was conducted to adapt the Grief and Meaning Reconstruction Inventory (GMRI) into Spanish and to establish its psychometric properties in a group of 202 mourners seeking treatment in Spain. A confirmatory factor analysis established that the Spanish GMRI yielded eight factors, displaying strong reliability and convergent validity, through the scale's inverse correlation with the Inventory of Complicated Grief and various measures of psychological distress, explaining a 48% of the variance of the former measure. A network analysis suggested that sense-making could play a central role in meaning-making during bereavement.

Patterns of change and their relationship to outcome and follow-up in group and individual psychotherapy for depression.

OBJECTIVE: The study explored the presence of different patterns of change in a sample of patients who received cognitive therapy for depression sequentially in two different formats: group and individual. Our hypothesis was that patients' baseline characteristics (e.g., symptom severity, self-esteem) would discriminate patterns of response to group and individual therapy. METHOD: 108 adults who met criteria for depression and completed the treatments included in a randomized controlled trial (RCT) were assessed with the Beck Depression Inventory-II (BDI-II), the Clinical Outcome in Routine Evaluation-Short Form B (CORE-SFB), the Global Assessment of Functioning (GAF), and the repertory grid technique. Growth mixture modeling was carried out to identify the patterns of change. Mixed linear models and repeated measures analysis of variance were performed to compare patients' characteristics in each pattern. Multinomial logistic regression was used to compute predictive models for the patterns from patients' characteristics. Finally, hierarchical linear regression was used to establish the power of each pattern to predict treatment outcome. RESULTS: A 3-class solution was obtained: group therapy improvers, individual therapy improvers, and nonimprovers. Group therapy improvers started therapy with less severe levels of depression and psychological distress, higher functioning and self-esteem, lower perceived social isolation, and lower dilemmatic construction of the self than the other groups. Individual therapy improvers and nonimprovers presented similar characteristics at baseline. However, a significant proportion of nonimprovers presented a concurrent diagnosis of fibromyalgia. CONCLUSION: The greater the impairment that patients present at baseline, the more likely they are to benefit from individual therapy after group therapy. A diagnosis of fibromyalgia can be considered a risk factor for therapy failure in the treatment of depression. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Psychometric Properties of the Outcome Rating Scale (ORS) in a Spanish Clinical Sample.

The Outcome Rating Scale (ORS) is an ultra-brief measure of well-being designed to track outcome in psychotherapy. This research studied the psychometric properties of the ORS in a Spanish clinical sample. One-hundred and sixty-five adult participants from different primary care centers of the city of Barcelona were recruited. The psychometric properties of the ORS in the sample were explored and described, comparing them to the properties of other instruments already validated in Spain. Our results showed good reliability (α = .91 [.88, .93]; α = .96; test re-test correlations from .61 to .84), good validity (convergent validity correlations with distress and symptoms measures from -.32 to -.76), and good sensitivity to change (pre-post comparison through Wilcoxon signed-rank test, Mdnpre = 31.0, Mdnpost = 19.6, z = -7.38, p < .05, r = .42). These results are consistent with previous findings in other countries. We conclude suggesting that the instrument can be applied to monitor outcome in psychotherapy and to test the effectiveness of treatments imparted with Spanish speaking clients. However, further research with the ORS in Spanish could provide more evidence of its psychometric properties.