Using Progress Feedback to Enhance Treatment Outcomes: A Narrative Review.

We face increasing demand for greater access to effective routine mental health services, including telehealth. However, treatment outcomes in routine clinical practice are only about half the size of those reported in controlled trials. Progress feedback, defined as the ongoing monitoring of patients' treatment response with standardized measures, is an evidence-based practice that continues to be under-utilized in routine care. The aim of the current review is to provide a summary of the current evidence base for the use of progress feedback, its mechanisms of action and considerations for successful implementation. We reviewed ten available meta-analyses, which report small to medium overall effect sizes. The results suggest that adding feedback to a wide range of psychological and psychiatric interventions (ranging from primary care to hospitalization and crisis care) tends to enhance the effectiveness of these interventions. The strongest evidence is for patients with common mental health problems compared to those with very severe disorders. Effect sizes for not-on-track cases, a subgroup of cases that are not progressing well, are found to be somewhat stronger, especially when clinical support tools are added to the feedback. Systematic reviews and recent studies suggest potential mechanisms of action for progress feedback include focusing the clinician's attention, altering clinician expectations, providing new information, and enhancing patient-centered communication. Promising approaches to strengthen progress feedback interventions include advanced systems with signaling technology, clinical problem-solving tools, and a broader spectrum of outcome and progress measures. An overview of methodological and implementation challenges is provided, as well as suggestions for addressing these issues in future studies. We conclude that while feedback has modest effects, it is a small and affordable intervention that can potentially improve outcomes in psychological interventions. Further research into mechanisms of action and effective implementation strategies is needed.

What time-varying network models based on functional analysis tell us about the course of a patient's problem.

Background: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing.

Exploring the complex interrelation between depressive symptoms, risk, and protective factors: A comprehensive network approach.

BACKGROUND: Depressive symptoms seem to be interrelated in a complex and self-reinforcing way. To gain a better understanding of this complexity, the inclusion of theoretically relevant constructs (such as risk and protective factors) offers a comprehensive view into the complex mechanisms underlying depression. METHODS: Cross-sectional data from individuals diagnosed with a major depressive disorder (N = 986) and healthy controls (N = 1049) were analyzed. Participants self-reported their depressive symptoms, as well as several risk factors and protective factors. Regularized partial correlation networks were estimated for each group and compared using a network comparison test. RESULTS: Symptoms of depression were more strongly connected in the network of depressed patients than in healthy controls. Among the risk factors, perceived stress, the experience of negative life events, emotional neglect, and emotional abuse were the most centrally embedded in both networks. However, the centrality of risk factors did not significantly differ between the two groups. Among the protective factors, social support, personal competence, and acceptance were the most central in both networks, where the latter was significantly more strongly associated with the symptom of self-hate in depressed patients. CONCLUSION: The network analysis revealed that key symptoms of depression were more strongly connected for depressed patients than for healthy controls, and that risk and protective factors play an important role, particularly perceived stress in both groups and an accepting attitude for depressed patients. However, the purpose of this study is hypothesis generating and assisting in the potential selection of non-symptom nodes for future research.

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.

To Use or Not to Use: Exploring Therapists' Experiences with Pre-Treatment EMA-Based Personalized Feedback in the TheraNet Project.

BACKGROUND: Using idiographic network models in psychotherapy has been a growing area of interest. However, little is known about the perceived clinical utility of network models. The present study aims to explore therapists' experiences with network model-based feedback within the context of the TheraNet Project. METHODS: In total, 18 therapists who had received network-based feedback for at least 1 patient at least 2 months prior were invited to retrospective focus groups. The focus group questions related to how participation in the study influenced the therapeutic relationship, how the networks were used, and what might improve their clinical utility. The transcribed focus groups were analyzed descriptively using qualitative content analysis. RESULTS: Most therapists mentioned using the feedback to support their existingtheir case concept, while fewer therapists discussed the feedback directly with the patients. Several barriers to using the feedback were discussed, as well as various suggestions for how to make it more clinically useful. Many therapists reported skepticism with regards to research in the outpatient training center in general, though they were also all pleasantly surprised by being involved, having their opinions heard, and showing a readiness to adapt research to their needs/abilities. CONCLUSIONS: This study highlights the gap between researchers' and therapists' perceptions about what useful feedback should look like. The TheraNet therapists' interest in adapting the feedback and building more informative feedback systems signals a general openness to the implementation of clinically relevant research. We provide suggestions for future implementations of network-based feedback systems in the outpatient clinical training center setting.

Dynamic and static predictive modelling of psychotherapy outcome-Comparison of two statistical approaches.

OBJECTIVE: Improving prediction abilities in the therapy process can increase therapeutic success for a variety of reasons, such as more personalised treatment or resource optimisation. The increasingly applied methods of dynamic prediction seem to be very promising for this purpose. Prediction models are usually based on static approaches of frequentist statistics. However, the application of this statistical approach has been widely criticised in this research area. Bayesian statistics has been proposed in the literature as an alternative, especially for the task of dynamic modelling. In this study, we compare the performance of predicting therapy outcome over the course of therapy between both statistical approaches. METHOD: Based on a sample of 341 patients, a logistic regression analysis was performed using both statistical approaches. Therapy success was conceptualised as reliable pre-post improvement in brief symptom inventory (BSI) scores. As predictors, we used the subscales of the Outcome Questionnaire (OQ-30) and the Helping Alliance Questionnaire (HAQ) measured every fifth session, as well as baseline BSI scores. RESULTS: The influence of the predictors during therapy differs between the frequentist and the Bayesian approach. In contrast, predictive validity is comparable with a mean area under the curve (AUC) of 0.76 in both model types. CONCLUSION: Bayesian statistic provides an innovative and useful alternative to the frequentist approach in predicting therapy outcome. The theoretical foundation is particularly well suited for dynamic prediction. Nevertheless, no differences in predictive validity were found in this study. More complex methodology as well as further research seems necessary to exploit the potential of Bayesian statistics in this area.

A Clinical Leadership Lens on Implementing Progress Feedback in Three Countries: Development of a Multidimensional Qualitative Coding Scheme.

BACKGROUND: Progress feedback, also known as measurement-based care (MBC), is the routine collection of patient-reported measures to monitor treatment progress and inform clinical decision-making. Although a key ingredient to improving mental health care, sustained use of progress feedback is poor. Integration into everyday workflow is challenging, impacted by a complex interrelated set of factors across patient, clinician, organizational, and health system levels. This study describes the development of a qualitative coding scheme for progress feedback implementation that accounts for the dynamic nature of barriers and facilitators across multiple levels of use in mental health settings. Such a coding scheme may help promote a common language for researchers and implementers to better identify barriers that need to be addressed, as well as facilitators that could be supported in different settings and contexts. METHODS: Clinical staff, managers, and leaders from two Dutch, three Norwegian, and four mental health organizations in the USA participated in semi-structured interviews on how intra- and extra-organizational characteristics interact to influence the use of progress feedback in clinical practice, supervision, and program improvement. Interviews were conducted in the local language, then translated to English prior to qualitative coding. RESULTS: A team-based consensus coding approach was used to refine an a priori expert-informed and literature-based qualitative scheme to incorporate new understandings and constructs as they emerged. First, this hermeneutic approach resulted in a multi-level coding scheme with nine superordinate categories and 30 subcategories. Second-order axial coding established contextually sensitive categories for barriers and facilitators. CONCLUSIONS: The primary outcome is an empirically derived multi-level qualitative coding scheme that can be used in progress feedback implementation research and development. It can be applied across contexts and settings, with expectations for ongoing refinement. Suggestions for future research and application in practice settings are provided. Supplementary materials include the coding scheme and a detailed playbook.

On the current psychotherapeutic situation for persons with pornography use disorder in Germany.

Background and aims: For the first time, the ICD-11 provides the diagnosis compulsive sexual behavior disorder (CSBD) that can be assigned for pornography use disorder (PUD). This study aimed to estimate the prevalence of PUD and associated consequences in Germany, to identify the psychotherapy demand among likely PUD (lPUD) cases and the treatment supply in different psychotherapeutic settings, to survey psychotherapists' level of expertise regarding PUD, and to identify predictors for psychotherapy demand. Methods: Four studies were conducted: 1. Online study in the general population (n = 2070; m = 48.9%, f = 50.8%, d = 0.2%), 2. Survey among practicing psychotherapists (n = 983), 3. Survey of psychotherapists in psychotherapeutic outpatient clinics (n = 185), 4. Interviews with psychotherapeutic inpatient clinics (n = 28). Results: The estimated prevalence of lPUD in the online study was 4.7% and men were 6.3 times more often affected than women. Compared to individuals without PUD, individuals with lPUD more often indicated negative consequences in performance-related areas. Among lPUD cases, 51.2% of men and 64.3% of women were interested in a specialized PUD treatment. Psychotherapists reported 1.2%-2.9% of lPUD cases among their patients. 43.2%-61.5% of psychotherapists stated to be poorly informed about PUD. Only 7% of psychotherapeutic inpatient clinics provided specific treatments to patients with PUD. While, among other factors, negative consequences attributed to lPUD were predictive for psychotherapy demand, weekly pornography consumption, subjective well-being, and religious attachment were not. Discussion and conclusions: Although PUD occurs quite often in Germany, availability of mental health care services for PUD is poor. Specific PUD treatments are urgently needed.

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.

Movement-based patient-therapist attunement in psychological therapy and its association with early change.

Objective: Attunement is a novel measure of nonverbal synchrony reflecting the duration of the present moment shared by two interaction partners. This study examined its association with early change in outpatient psychotherapy. Methods: Automated video analysis based on motion energy analysis (MEA) and cross-correlation of the movement time-series of patient and therapist was conducted to calculate movement synchrony for N = 161 outpatients. Movement-based attunement was defined as the range of connected time lags with significant synchrony. Latent change classes in the HSCL-11 were identified with growth mixture modeling (GMM) and predicted by pre-treatment covariates and attunement using multilevel multinomial regression. Results: GMM identified four latent classes: high impairment, no change (Class 1); high impairment, early response (Class 2); moderate impairment (Class 3); and low impairment (Class 4). Class 2 showed the strongest attunement, the largest early response, and the best outcome. Stronger attunement was associated with a higher likelihood of membership in Class 2 (b = 0.313, p = .007), Class 3 (b = 0.251, p = .033), and Class 4 (b = 0.275, p = .043) compared to Class 1. For highly impaired patients, the probability of no early change (Class 1) decreased and the probability of early response (Class 2) increased as a function of attunement. Conclusions: Among patients with high impairment, stronger patient-therapist attunement was associated with early response, which predicted a better treatment outcome. Video-based assessment of attunement might provide new information for therapists not available from self-report questionnaires and support therapists in their clinical decision-making.

How to model and interpret cross-lagged effects in psychotherapy mechanisms of change research: A comparison of multilevel and structural equation models.

OBJECTIVE: Modeling cross-lagged effects in psychotherapy mechanisms of change studies is complex and requires careful attention to model selection and interpretation. However, there is a lack of field-specific guidelines. We aimed to (a) describe the estimation and interpretation of cross lagged effects using multilevel models (MLM) and random-intercept cross lagged panel model (RI-CLPM); (b) compare these models' performance and risk of bias using simulations and an applied research example to formulate recommendations for practice. METHOD: Part 1 is a tutorial focused on introducing/describing dynamic effects in the form of autoregression and bidirectionality. In Part 2, we compare the estimation of cross-lagged effects in RI-CLPM, which takes dynamic effects into account, with three commonly used MLMs that cannot accommodate dynamics. In Part 3, we describe a Monte Carlo simulation study testing model performance of RI-CLPM and MLM under realistic conditions for psychotherapy mechanisms of change studies. RESULTS: Our findings suggested that all three MLMs resulted in severely biased estimates of cross-lagged effects when dynamic effects were present in the data, with some experimental conditions generating statistically significant estimates in the wrong direction. MLMs performed comparably well only in conditions which are conceptually unrealistic for psychotherapy mechanisms of change research (i.e., no inertia in variables and no bidirectional effects). DISCUSSION: Based on conceptual fit and our simulation results, we strongly recommend using fully dynamic structural equation modeling models, such as the RI-CLPM, rather than static, unidirectional regression models (e.g., MLM) to study cross-lagged effects in mechanisms of change research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Applying a web-based self-help intervention for bulimia nervosa in routine care: Study protocol for a randomized controlled trial.

BACKGROUND: Individuals with bulimia nervosa (BN) experience persistent episodes of binge eating and inappropriate compensatory behavior associated with impaired physical and mental health. Despite the existence of effective treatments, many individuals with BN remain untreated, leading to a high burden and an increased risk of chronicity. Web-based interventions may help facilitate access to evidence-based treatments for BN by reducing barriers to the health care system. METHODS: The present study will investigate the effectiveness of a web-based self-help intervention for BN in a two-armed randomized controlled trial. Individuals diagnosed with BN (N = 152) will be randomly assigned to either (1) an intervention group receiving a 12-week web-based intervention or (2) a waitlist control group with delayed access to the intervention. Further assessments will be scheduled 6 (mid-treatment) and 12 (post-treatment) weeks after baseline. Changes in the number of binge eating episodes and compensatory behaviors will be examined as primary outcomes. Secondary outcomes include global eating pathology, functional impairments, well-being, comorbid psychopathology, self-esteem, and emotion regulation abilities. DISCUSSION: Adding web-based interventions into routine care is a promising approach to overcome the existing treatment gap for patients with BN. Therefore, the current study will test the effectiveness of a web-based intervention for BN under standard clinical care settings. TRIAL REGISTRATION: ClinicalTrials.gov, Identifier: NCT04876196 (registered on May 6th, 2021).

Integrating a web-based intervention into routine care of binge-eating disorder: Study protocol for a randomized controlled trial.

Background: Although binge eating disorder (BED) is the most common eating pathology and carries a high mental and physical burden, access to specialized treatment is limited due to patient-related barriers and insufficient healthcare resources. Integrating web-based self-help programs into clinical care for BED may address this treatment gap by making evidence-based eating disorder interventions more accessible. Methods: A two-armed randomized controlled trial will be conducted to evaluate the effectiveness of a web-based self-help intervention for BED in routine care settings. Patients aged 18-65 years fulfilling the diagnostic criteria for BED (N = 152) will be randomly allocated to (1) an intervention group receiving a 12-week web-based self-help program or (2) a waitlist control group with delayed access to the intervention. The primary outcome will be the number of binge eating episodes. Secondary outcomes include global eating pathology, functional impairments, work capacity, well-being, comorbid psychopathology, self-esteem, and emotion regulation abilities. Measurements will be conducted at baseline (study entrance), 6 weeks after baseline (mid-treatment), and 12 weeks after baseline (post-treatment). To capture outcomes and treatment mechanisms in real-time, traditional self-reports will be combined with weekly symptom monitoring and ecological momentary assessment. Discussion: Evaluating the effectiveness of web-based interventions is essential to overcome the treatment gap for patients with BED. When adequately integrated into standard care, these programs have the potential to alleviate the high burden of BED for individuals, their families, and society. Trial registration: https://clinicaltrials.gov/ct2/show/NCT04876183, Identifier: NCT04876183 (registered on May 6th, 2021).

Symptom change trajectories in patients with persistent somatic symptoms and their association to long-term treatment outcome.

OBJECTIVE: This study investigated symptom change trajectory for patients with persistent somatic symptoms (PSS) during psychotherapy and the association of these patterns with pre-treatment characteristics and long-term outcome. METHODS: Growth mixture modeling was used to identify trajectory curves in a sample of N = 210 outpatients diagnosed with PSS and treated either with conventional cognitive behavioral therapy (CBT) or CBT enriched with emotion regulation training (ENCERT). RESULTS: We identified three subgroups of patients with similar symptom change patterns over the course of treatment (a "no change," "strong response," and "slow change" subgroup). Higher initial anxiety symptoms were significantly associated with the no change and strong response subgroups; symptom-related disability in daily routine with no changes. Patients with a strong response had the highest proportion of reliable improvement at termination and at six-month-follow-up. CONCLUSION: Our results indicate that, instead of one common change pattern, patients with PSS respond differently to treatment. Due to the high association of symptom curves with long-term outcome, the identification and prediction of an individual's trajectory could provide important information for clinicians to identify non-responding patients that are at risk for failure. Selecting personalized treatment interventions could increase the effectiveness of psychotherapy.Trial registration: ClinicalTrials.gov identifier: NCT01908855..

For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization.

OBJECTIVE: We aimed to develop and test an algorithm for individual patient predictions of problem coping experiences (PCE) (i.e., patients' understanding and ability to deal with their problems) effects in cognitive-behavioral therapy. Method: In an outpatient sample with a variety of diagnoses (n=1010), we conducted Dynamic Structural Equation Modelling to estimate within-patient cross-lagged PCE effects on outcome during the first ten sessions. In a randomly selected training sample (2/3 of the cases), we tried different machine learning algorithms (i.e., ridge regression, LASSO, elastic net, and random forest) to predict PCE effects (i.e., the degree to which PCE was a time-lagged predictor of symptoms), using baseline demographic, diagnostic, and clinically-relevant patient features. Then, we validated the best algorithm on a test sample (1/3 of the cases). RESULTS: The random forest algorithm performed best, explaining 14.7% of PCE effects variance in the training set. The results remained stable in the test set, explaining 15.4% of PCE effects variance. CONCLUSIONS: The results show the suitability to perform individual predictions of process effects, based on patients' initial information. If the results are replicated, the algorithm might have the potential to be implemented in clinical practice by integrating it into monitoring and therapist feedback systems.

Predicting optimal treatment outcomes using the Personalized Advantage Index for patients with persistent somatic symptoms.

Objective: Because individual patients with persistent somatic symptoms (PSS) respond differently to treatments, a better understanding of the factors that predict therapy outcomes are of high importance. Aggregating a wide selection of information into the treatment-decision process is a challenge for clinicians. Using the Personalized Advantage Index (PAI) this study aims to deal with this. Methods: Data from a multicentre RCT comparing CBT (N = 128) versus CBT enriched with emotion regulation training (ENCERT) (N = 126) for patients diagnosed with somatic symptom disorder were used to identify based on two machine learning approaches predictors of therapy outcomes. The identified predictors were used to calculate the PAI. Results: Five treatment unspecific predictors (pre-treatment somatic symptom severity, depression, symptom disability, health-related quality of life, age) and five treatment specific moderators (global functioning, early childhood traumatic events, gender, health anxiety, emotion regulation skills) were identified. Individuals assigned to their PAI-indicated optimal treatment had significantly lower somatic symptom severity at the end of therapy compared to those randomised to their non-optimal condition. Conclusion: Allowing patients to choose a personalised treatment seems to be meaningful. This could help to improve outcomes for PSS and reduce its high costs to the health care system.

Are some therapists better at facilitating and consolidating sudden gains than others?

BACKGROUND: Changes during psychotherapy often include sudden symptom improvements, called sudden gains (SGs), which have been identified as being superior to gradual symptom change with regard to treatment success. This study investigates the role of therapists in initiating and/or consolidating SGs. METHODS: The analyses are based on a sample of patients (N = 1937) who were seen by 155 therapists and received individual psychotherapy at a university outpatient clinic. First, the therapist effect (TE) on SG was investigated using multilevel modeling (MLM). Second, MLM was used to explore the relative importance of patient and therapist variability in SGs as they relate to outcome. RESULTS: The TE on SGs accounted for 1.8% of variance, meaning that therapists are accountable for inter-individual differences in their patients' likelihood to experience SGs. Furthermore, results revealed a significant effect of SGs on outcome for both levels, while therapist differences regarding the consolidation of SGs were not significant. CONCLUSIONS: The analyses indicated that some therapists are better in facilitating and initiating SGs. The process of triggering SGs seems to be a therapist skill or competence, which opens up an additional pathway to positive outcomes that could be used to improve clinical training.

A Systematic Review of Momentary Assessment Designs for Mood and Anxiety Symptoms.

Background: Altering components of ecological momentary assessment (EMA) measures to better suit the purposes of individual studies is a common and oftentimes necessary step. Though the inherent flexibility in EMA has its benefits, no resource exists to provide an overview of the variability in how convergent constructs and symptoms have been assessed in the past. The present study fills that gap by examining EMA measurement design for mood and anxiety symptomatology. Methods: Various search engines were used to identify 234 relevant studies. Items administered, data collection schedules (i.e., beeps per day), response scales (i.e., Likert), data collection platforms (i.e., apps), and psychometric properties (i.e., reliability) were extracted. Results: Study designs varied greatly in all aspects across the identified papers. Over 4,600 extracted items were qualitatively analyzed, resulting in the identification of 12 themes. The most EMA items focused on affect, with categories such as "happiness" and "tension" appearing most frequently. We provide all of our data extraction in the format of an open-source database. Limitations: Despite our best attempts to include as much of the relevant literature as possible, this review and the accompanying database are not exhaustive, but can easily be built upon to include other, newer studies. Conclusions: The fact that the affect theme featured both positive and negative emotional constructs highlights the dichotomous focus on valence and affect within the literature surrounding anxious and depressive symptomatology. We hope that our database will act as a helpful design decision-making resource for researchers studying this kind of symptomatology in the future. Systematic Review Registration: PROSPERO (CRD42019139409).

The Use of Digitally Assessed Stress Levels to Model Change Processes in CBT - A Feasibility Study on Seven Case Examples.

In psychotherapy research, the measurement of treatment processes and outcome are predominantly based on self-reports. However, given new technological developments, other potential sources can be considered to improve measurements. In a feasibility study, we examined whether Ecological Momentary Assessments (EMA) using digital phenotyping (stress level) can be a valuable tool to investigate change processes during cognitive behavioral therapy (CBT). Seven outpatients undergoing psychological treatment were assessed using EMA. Continuous stress levels (heart rate variability) were assessed via fitness trackers (Garmin) every 3 min over a 2-week time period (6,720 measurements per patient). Time-varying change point autoregressive (TVCP-AR) models were employed to detect both gradual and abrupt changes in stress levels. Results for seven case examples indicate differential patterns of change processes in stress. More precisely, inertia of stress level changed gradually over time in one of the participants, whereas the other participants showed both gradual and abrupt changes. This feasibility study demonstrates that intensive longitudinal assessments enriched by digitally assessed stress levels have the potential to investigate intra- and interindividual differences in treatment change processes and their relations to treatment outcome. Further, implementation issues and implications for future research and developments using digital phenotyping are discussed.