A clinical PREMISE for personalized models: Toward a formal integration of case formulations and statistical networks

Mindfulness as a therapeutic option for obsessive-compulsive disorder

INTRODUCTION

Obsessive-compulsive disorder (OCD) is a prevalent mental health issue characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions) that can cause significant life impairment. Despite cognitive-behavioral therapy (CBT) being the most effective treatment, some individuals experience insufficient symptom reduction or relapse.

AREAS COVERED

This special report explores the potential of mindfulness-based interventions as complementary treatments for OCD, examining the specific techniques used and their practical application. In the initial section, the authors examine ten randomized control trial studies included in the meta-analysis conducted by Chien et al. (2022), demonstrating the effectiveness of mindfulness interventions. The authors focus on elucidating the specific mindfulness techniques used in these studies. Then, the authors discuss the integration of these mindfulness strategies into CBT, focusing on enhancing emotional regulation, cognitive flexibility, and acceptance of intrusive thoughts.

EXPERT OPINION

While mindful based interventions (MBIs) show promise as adjunctive treatments for OCD, variability in OCD symptoms and treatment responses necessitate individualized therapeutic approaches. Further research is required to refine mindfulness-based techniques and optimize their effectiveness. Incorporating MBIs into standard CBT protocols may improve outcomes for patients with persistent OCD symptoms.

Symptoms of a feather flock together? An exploratory secondary dynamic time warp analysis of 11 single case time series of suicidal ideation and related symptoms

Suicidal ideation fluctuates over time, as does its related risk factors. Little is known about the difference or similarities of the temporal patterns. The current exploratory secondary analysis examines which risk symptoms have similar time dynamics using a mathematical algorithm called dynamic time warping (DTW). Ecological momentary assessment data was used of 11 depressed psychiatric outpatients with suicidal ideation who answered three daytime surveys at semi-random sampling points for a period of three to six months. Patients with 45 assessments or more were included. Results revealed significant inter-individual variability in symptom dynamics and clustering, with certain symptoms often clustering due to similar temporal patterns, notably feeling sad, hopelessness, feeling stuck, and worrying. The directed network analyses shed light on the temporal order, highlighting entrapment and worrying as symptoms strongly related to suicide ideation. Still, all patients also showed unique directed networks. While for some patients changes in entrapment directly preceded change in suicide ideation, the reverse temporal ordering was also found. Relatedly, within some patients, perceived burdensomeness played a pivotal role, whereas in others it was unconnected to other symptoms. The study underscores the individualized nature of symptom dynamics and challenges linear models of progression, advocating for personalized treatment strategies.

Introducing FRED: Software for Generating Feedback Reports for Ecological Momentary Assessment Data

Ecological Momentary Assessment (EMA) is a data collection approach utilizing smartphone applications or wearable devices to gather insights into daily life. EMA has advantages over traditional surveys, such as increasing ecological validity. However, especially prolonged data collection can burden participants by disrupting their everyday activities. Consequently, EMA studies can have comparably high rates of missing data and face problems of compliance. Giving participants access to their data via accessible feedback reports, as seen in citizen science initiatives, may increase participant motivation. Existing frameworks to generate such reports focus on single individuals in clinical settings and do not scale well to large datasets. Here, we introduce FRED (Feedback Reports on EMA Data) to tackle the challenge of providing personalized reports to many participants. FRED is an interactive online tool in which participants can explore their own personalized data reports. We showcase FRED using data from the WARN-D study, where 867 participants were queried for 85 consecutive days with four daily and one weekly survey, resulting in up to 352 observations per participant. FRED includes descriptive statistics, time-series visualizations, and network analyses on selected EMA variables. Participants can access the reports online as part of a Shiny app, developed via the R programming language. We make the code and infrastructure of FRED available in the hope that it will be useful for both research and clinical settings, given that it can be flexibly adapted to the needs of other projects with the goal of generating personalized data reports.

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

BACKGROUND

As depression is highly heterogenous, an increasing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time invariant, which may lead to important temporal features of the disorder being missed.

OBJECTIVE

To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time.

METHODS

Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models.

RESULTS

The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants.

CONCLUSIONS

Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes.

Competence-Based Trainings for Psychological Treatments - A Transtheoretical Perspective

Background

Although in most countries psychotherapy trainings focus on one treatment orientation, such an approach is associated with systematic shortcomings. The priorities from teaching one theoretical framework should be moved to a more rigorous orientation in science and evidence-based practice, and to the needs of patients, even if strategies of different theoretical approaches need to be combined.

Method

We discuss whether competence-based trainings in psychological treatments offer a better framework to facilitate the progress of psychological treatments to a professional academic discipline with transtheoretical exchange, and we provide an example of a transtheoretical education in the basic competences of psychological treatments. A transtheoretical education program requires an umbrella model for case formulation and a transtheoretical definition of intervention goals.

Results

We provide an adaptation of the traditional model distinguishing vulnerability/resilience, exacerbation, and maintenance of clinical problems for case conceptualization. Dynamic network models offer a further perspective for developing modern, transtheoretical case formulations. Treatment methods should be better classified according to their transtheoretical goals, which offers opportunities to better compare or combine them. We report a case example of how to transform a general competence-based approach in the training of psychological treatments in the academic education system, which found exceptional acceptance from participating students.

Conclusion

Thus, a rigorous competence-based approach to training early clinicians in applying psychological treatments helps to bridge the artificial divide between psychotherapeutic traditions. It also supports the evolution of psychological treatments into an academically robust and highly professional, integrative discipline.

Using the experience sampling method to support clinical practice: An illustration with problematic cannabis use

The experience sampling method (ESM) has been frequently used in clinical research; however, there is low translational uptake in clinical practice. This may be due to challenges with interpreting individual-level data at granular intervals. We provide an illustrative example of how ESM can be leveraged to generate personalized cognitive-behavioral strategies for problematic cannabis use.

We conducted a descriptive case series analysis using ESM data from 30 individuals reporting on problematic cannabis use, craving, affect, and coping four times daily for 16-days (t = 64, T = 1,920).

Analyzing ESM data using descriptive statistics and visualizations from individuals with similar clinical and demographic profiles supported a diverse array of personalized clinical insights and recommendations for each case. These recommendations included psychoeducation regarding affect- and boredom-regulation strategies, functional analyses of occasions during which cannabis was not used, and discussions on how cannabis use intersects with one's personal values.

While many clinicians utilize measurement-based care, barriers have limited the incorporation of ESM towards personalized, data-informed approaches to treatment. We provide an illustrative example of how ESM data can be used to generate actionable treatment strategies for problematic cannabis use and highlight continued challenges with interpreting time-series data.

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making. This study describes an innovative type of data-driven personalization in the context of StayFine, a guided app-based relapse prevention intervention for 13- to 21-year-olds in remission of anxiety or depressive disorders (n = 74). Participants receive six modules, of which three are chosen from five optional modules. Optional modules are Enhancing Positive Affect, Behavioral Activation, Exposure, Sleep, and Wellness. All participants receive Psycho-Education, Cognitive Restructuring, and a Relapse Prevention Plan. The personalization approach is based on four sources: (1) prior diagnoses (diagnostic interview), (2) transdiagnostic psychological factors (online self-report questionnaires), (3) individual symptom networks (ecological momentary assessment, based on a two-week diary with six time points per day), and subsequently, (4) patient preference based on shared decision-making with a trained expert by experience. This study details and evaluates this innovative type of personalization approach, comparing the congruency of advised modules between the data-driven sources (1-3) with one another and with the chosen modules during the shared decision-making process (4). The results show that sources of data-driven personalization provide complementary advice rather than a confirmatory one. The indications of the modules Exposure and Behavioral Activation were mostly based on the diagnostic interview, Sleep on the questionnaires, and Enhancing Positive Affect on the network model. Shared decision-making showed a preference for modules improving positive concepts rather than combating negative ones, as an addition to the data-driven advice. Future studies need to test whether treatment outcomes and dropout rates are improved through personalization.

Which symptom to address in psychological treatment for cancer survivors when fear of cancer recurrence, depressive symptoms, and cancer-related fatigue co-occur? Exploring the level of agreement between three systematic approaches to select the focus of treatment

PURPOSE

To investigate the extent to which three systematic approaches for prioritizing symptoms lead to similar treatment advices in cancer survivors with co-occurring fear of cancer recurrence, depressive symptoms, and/or cancer-related fatigue.

METHODS

Psychological treatment advices were was based on three approaches: patient preference, symptom severity, and temporal precedence of symptoms based on ecological momentary assessments. The level of agreement was calculated according to the Kappa statistic.

RESULTS

Overall, we found limited agreement between the three approaches. Pairwise comparison showed moderate agreement between patient preference and symptom severity. Most patients preferred treatment for fatigue. Treatment for fear of cancer recurrence was mostly indicated when based on symptom severity. Agreement between temporal precedence and the other approaches was slight. A clear treatment advice based on temporal precedence was possible in 57% of cases. In cases where it was possible, all symptoms were about equally likely to be indicated.

CONCLUSIONS

The three approaches lead to different treatment advices. Future research should determine how the approaches are related to treatment outcome. We propose to discuss the results of each approach in a shared decision-making process to make a well-informed and personalized decision with regard to which symptom to target in psychological treatment.

IMPLICATIONS FOR CANCER SURVIVORS

This study contributes to the development of systematic approaches for selecting the focus of psychological treatment in cancer survivors with co-occurring symptoms by providing and comparing three different systematic approaches for prioritizing symptoms.