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.

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.

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.