Convergent Validation of Methods for the Identification of Psychotherapeutic Phase Transitions in Time Series of Empirical and Model Systems

Features of Self-Organization during the Process of Mindfulness-Based Stress Reduction: A Single Case Study

Compared to the extensive evidence of the effectiveness of mindfulness-based interventions, there is only a limited understanding of their mechanisms of change. The three aims of this study are (1) to identify features of self-organization during the process (e.g., pattern transitions), (2) to obtain an impression of the effects of continuous self-assessments and feedback sessions on mindfulness-related stress reduction, and (3) to test the feasibility of high-frequency process monitoring and process feedback. Concerning aim (1), the specific hypothesis is that change will occur as a cascade of discontinuous pattern transitions emerging spontaneously in the sense of not being a reaction to external input. This single case study describes changing patterns of multiple time series that were produced by app-based daily self-assessments during and after an 8-week mindfulness-based stress reduction program. After this MBSR program, the participant (a female nurse) continued the self-assessment and the mindfulness practice for a further 10 months. The results confirm findings on the positive effects of mindfulness programs for healthcare professionals, especially on coping with work-related stress. The analysis of the time series data supports the hypothesis of self-organization as a possible mechanism of change manifesting as a cascade of phase transitions in the dynamics of a biopsychosocial system. At the end of the year, the participant reported a beneficial impact of daily monitoring and systematic feedback on the change process. The results underline the feasibility and usefulness of continuous high-frequency monitoring during and after mindfulness interventions.

Emotion Regulation Flexibility and Electronic Patient-Reported Outcomes: A Framework for Understanding Symptoms and Affect Dynamics in Pediatric Psycho-Oncology

Simple Summary

The individual’s ability to conceive and regulate the broad spectrum of their human emotions is closely linked to their mental health. The implications of a serious disease such as cancer represent an extraordinary burden to these internal coping mechanisms, especially in the case of young patients. Regarding their well-being and support, it is therefore of particular interest for caregivers to be able to follow the dynamics of the patient’s emotional world and perceptions. Technical progress enables new possibilities for data collection through tools for digital patient self-reports while simultaneously creating new challenges. Within the scope of this article, we provide an overview of the literature on this topic, outlining the current strengths and weaknesses and possible perspectives on digital aids, especially in terms of capturing the flexibility, fluctuations and early detection of symptom changes.

Abstract

Emotion dysregulation is regarded as a driving mechanism for the development of mental health problems and psychopathology. The role of emotion regulation (ER) in the management of cancer distress and quality of life (QoL) has recently been recognized in psycho-oncology. The latest technological advances afford ways to assess ER, affective experiences and QoL in child, adolescent and young adult (CAYA) cancer patients through electronic patient-reported outcomes (ePRO) in their daily environment in real-time. Such tools facilitate ways to study the dynamics of affect and the flexibility of ER. However, technological advancement is not risk-free. We critically review the literature on ePRO in cancer existing models of ER in pediatric psycho-oncology and analyze strength, weaknesses, opportunities and threats of ePRO with a focus on CAYA cancer research and care. Supported by personal study-based experiences, this narrative review serves as a foundation to propose a novel methodological and metatheoretical framework based on: (a) an extended notion of ER, which includes its dynamic, adaptive and flexible nature and focuses on processes and conditions rather than fixed categorical strategies; (b) ePRO as a means to measure emotion regulation flexibility and affect dynamics; (c) identifying early warning signals for symptom change via ePRO and building forecasting models using dynamical systems theory.

Stability and flexibility in psychotherapy process predict outcome

Ten good outcome and ten poor outcome psychotherapy cases were compared to investigate whether or not the temporal stability and flexibility of their process variables can predict their outcomes. Each participant was monitored daily using the Therapy Process Questionnaire (TPQ), which has 43 items and seven sub-scales, and responses over time were analyzed in terms of correlation robustness and correlation variability across the TPQ sub-scales. "Correlation robustness" and "correlation variability" are two basic characteristics of any correlation matrix: the first is calculated as the sum of the absolute values of Pearson correlation coefficients, the second as the standard deviation of Pearson correlation coefficients. The results demonstrated that the patients within the poor outcome group had lower values on both variables, suggesting lower stability and flexibility. Furthermore, a higher number of cycles of increase and decrease in correlation robustness and variability of the TPQ sub-scales was observed within good outcome psychotherapies, suggesting that, these cycles can be considered as process-markers of good-outcomes. These results provide support for the validity of these quantitative process-parameters, correlation robustness and variability, in predicting psychotherapeutic outcomes. Moreover, the results lend support to the common clinical experience of alternating periods of flexibility and integration being beneficial to good psychotherapeutic processes.

Prospects for Augmenting Team Interactions with Real-Time Coordination-Based Measures in Human-Autonomy Teams

Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human-autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams' coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human-autonomy teams that augment team interactions using coordination-based measures.

The Emotion Dynamics Conundrum in Developmental Psychopathology: Similarities, Distinctions, and Adaptiveness of Affective Variability and Socioaffective Flexibility

A recent emphasis in developmental psychopathology research has been on emotion dynamics, or how emotional experience changes over time in response to context, and how those emotion dynamics affect psychosocial functioning. Two prominent emotion dynamics constructs have emerged in the developmental psychopathology literature: affective variability and socioaffective flexibility. Affective variability is most often measured using momentary methods (e.g., EMA) and is theorized to reflect reactivity and regulation in response to context, whereas socioaffective flexibility is typically measured in the context of parent-child interactions and theorized as the ability to move effectively through a range of affective states. Notably, affective variability is considered broadly maladaptive; however, socioaffective flexibility is theorized to be fundamentally adaptive. Despite these diametric views on adaptability, these two constructs share an underlying dependency on non-effortful emotion change in response to context, which raises questions about whether these constructs are, at their core, more similar than dissimilar. This review examined the literatures on affective variability and socioaffective flexibility in child and adolescent samples, examining associations with psychosocial and clinical correlates, as well as conceptual and methodological similarities and distinctions. Findings indicate that despite considerable theoretical overlap, there are sufficient differences-albeit largely methodological-that justify continuing to treat these constructs as distinct, most notably the influence of parents in socioaffective flexibility. The review closes with several recommendations for future study targeted at further clarifying the distinctions (or lack thereof) between affective variability and socioaffective flexibility.

High-frequency monitoring of personalized psychological variables during outpatient psychotherapy in people with seizures: An uncontrolled feasibility study

BACKGROUND

This feasibility study applied the concept of daily systematic monitoring of personalized psychological variables and investigated patients' compliance in order to evaluate if its integration in outpatient psychotherapy is feasible and if patients found the development and daily application of personalized questionnaires user-friendly and useful.

METHODS

A naturalistic sample of patients with epilepsy (PWE) was enrolled to participate in an outpatient psychotherapy program. A personalized process questionnaire was developed with each patient based on an individual psychological system's model at the outset of therapy. Daily time-stamped self-assessments were collected during outpatient psychotherapy. This process-monitoring was technically realized by an internet-based device for data collection and data analysis, the Synergetic Navigation System (SNS). The reflection of person-specific time series informed by patients' replies to their personalized process questionnaire was integrated in the therapy process. Compliance rates were assessed during a period of six months (i.e., 180 days) after the first entry of the questionnaire [compliance rate = (number of completed questionnaires/180) × 100]. User-friendliness and usefulness of this process monitoring were evaluated quantitatively.

RESULTS

Twenty patients [15 women/5 men, median age 48 years (range 23-73 years)] were recruited. Compliance rates were high (median: 93%, range 31-100%) among the participants. Participants reported a high overall satisfaction with the application and user-friendliness of SNS.

CONCLUSION

The results support the feasibility of high-frequency monitoring of personalized psychological processes during outpatient psychotherapy. Repeated daily assessments of a personalized questionnaire yield highly resolved, equidistant time series data, which gives insight into individual psychological processes during outpatient psychotherapy.

Studying Behaviour Change Mechanisms under Complexity

Understanding the mechanisms underlying the effects of behaviour change interventions is vital for accumulating valid scientific evidence, and useful to informing practice and policy-making across multiple domains. Traditional approaches to such evaluations have applied study designs and statistical models, which implicitly assume that change is linear, constant and caused by independent influences on behaviour (such as behaviour change techniques). This article illustrates limitations of these standard tools, and considers the benefits of adopting a complex adaptive systems approach to behaviour change research. It (1) outlines the complexity of behaviours and behaviour change interventions; (2) introduces readers to some key features of complex systems and how these relate to human behaviour change; and (3) provides suggestions for how researchers can better account for implications of complexity in analysing change mechanisms. We focus on three common features of complex systems (i.e., interconnectedness, non-ergodicity and non-linearity), and introduce Recurrence Analysis, a method for non-linear time series analysis which is able to quantify complex dynamics. The supplemental website provides exemplifying code and data for practical analysis applications. The complex adaptive systems approach can complement traditional investigations by opening up novel avenues for understanding and theorising about the dynamics of behaviour change.