Abstract

Based on dynamical-systems theory, in the current study, we aimed to investigate if recurrence of depression is systematically preceded by within-persons early warning signals (EWSs) in positive and negative affect. Ecological momentary assessments were collected five times a day for a period of 4 months (averaging 524 assessments per individual) from 37 formerly depressed individuals discontinuing antidepressant medication. EWSs (increases in window autocorrelation and variance) preceded recurrence of depression in 32.9% of the participants across robustness checks. Compared with participants that remained in remission, participants with a recurrence showed significantly more positive trends in the variance but not in autocorrelation, and the average number of significant EWSs was more than 3 times larger across tested affect variables. Although the results provide the first systematic evidence that EWSs occur more often before the recurrence of depression, the low sensitivity of EWSs poses a substantial challenge for clinical applications.

Keywords: early warning signals, critical slowing down, dynamical systems theory, experience sampling methodology, depression, critical transitions

Abstract

The aim of this study was to develop a complex dynamic system (CDS) model of the therapeutic alliance and compare it to the currently dominant latent state–trait (LST) model. A clinical example of a state of alliance rupture and repair is analyzed in terms of a LST model and a CDS model. Then, the implications of these two models in their application to clinical research and practice are compared. Adopting a CDS perspective offers significant advantages over the LST approach. Specifically, it provides stronger clinical face validity, aligns with Bordin’s (1979) conceptualization of the alliance, accounts for the interaction of state and trait alliance, and assures conceptual independence between causal entities. By examining the interactions among observable reactions, the coordination of these reactions into patterns that form alliance states, and how iterative state changes reshape the trait alliance landscape of attractor states, the CDS perspective offers actionable clinical recommendations across all three levels. However, CDS and LST hybrid theories are possible and may have merit. The CDS perspective provides a holistic approach that requires a specification of all influential components of the target system—the disorder addressed—and of the therapy system—the extended system of therapeutic components. It leads to a research program for developing models of therapeutic systems case by case using qualitative and quantitative methods. It also provides precise tests of complete models, including comparison of mathematically derived simulations to real-world data and empirical testing by newly developed network analytic methods.

Keywords: therapeutic alliance, latent state-trait model, complex dynamic system model, clinical and research implications 

Abstract

Early warning signals are considered to be generic indicators of a system’s accumulating instability and ‘critical slowing down’ prior to substantial and abrupt transitions between stable states. In clinical psychology, these signals have been proposed to enable personalized predictions of the impending onset, recurrence and remission of mental health problems before changes in symptoms occur, thereby facilitating timely therapeutic interventions. In this Perspective, we question the idea that early warning signals in a person’s emotion time series can predict changes in mental health symptoms. Using the empirical findings to date and the theoretical and methodological limitations inherent in their application, we argue that there is little support for the use of early warning signals based on critical slowing down in clinical psychology. Deepening our knowledge of the theoretical foundations of these predictors and improving their measurement are key to clarifying the potential and boundaries for their use in psychopathology. It is necessary to build on the insights gained from early warning signal studies and to improve and evaluate alternative methods, keeping in mind that clinical applications require prospective, real-time predictions that not only indicate whether, but also when, a specific person is likely to experience changes in their mental health.

Keywords: dynamical systems; psychopathology; clinical change detection; early warning signals; critical slowing down

Abstract

Identifying relevant symptom shifts as they enfold can be challenging, as the time period over which they take place is not uniform for all people. This paper proposes an adaptation of the well-established Reliable Change Index (RCI) that allows researchers and clinicians to explore the presence of symptom changes of varying durations in individual patients’ time series: the Duration-adjusted RCI (DARCI). The DARCI takes the RCI cut-off score for change between two points as a starting point, and proportionally extends this measure over multiple observations, while maintaining reliability at a given confidence level. Researchers must choose the relevant time period between two observations, and additional increments are added accordingly.

To illustrate the ability of this method to detect changes of various durations, simulated depressive symptom time series with varying degrees of discontinuity and overall mean change in scores were used. The results show that the DARCI thresholds over two, three and four observations were effective at identifying the simulated change periods over multiple time points, starting from relatively gradual change slopes (picking up reliable changes in 20-60% of simulated time series if the overall change was large enough), to highly discontinuous changes (up to 100% accuracy).

The DARCI may be particularly useful for identifying shifts in symptoms that appear relatively abrupt, which can help indicate when a patient is showing significant improvement or deterioration. Its ease of use makes it suitable for application in the clinical context, and is a promising method to explore different change durations in clinical populations.

Keywords: duration of change; reliable change index (RCI); intensive longitudinal data; routine outcome measurements; symptom change; repeated assessments; within-person; threshold method; simulation

Abstract

Objective: Despite the importance for understanding mechanisms of change, little is known about the order of change in daily life emotions, cognitions, and behaviors during treatment of depression. This study examined the within-person temporal order of emotional, cognitive, and behavioral improvements using Ecological Momentary Assessment (EMA) data. 

Method: Thirty-two individuals with diagnosed depression completed EMA questions on emotions (sad mood, happy mood), behaviors (social interaction, number of activities), and cognitive variables (worrying, negative self-thoughts) five times a day during a four-month period in which they underwent psychotherapy for depression. Non-parametric change-point analyses were used to determine the timing of gains (i.e., improvements in the mean of each variable) for each individual. We then established whether the first (i.e., earliest) gains in emotions preceded, followed, or occurred in the same week as cognitive and behavioral gains. 

Results: Contrary to our hypotheses, first gains in behaviors did not precede first emotional gains (3 times, 8%) more often than they followed them (26 times, 70%). Cognitive gains often occurred in the same week as first emotional gains (43 times, 58%), and less often preceded (13 times; 18%) or followed emotional gains (18 times; 24%). 

Conclusion: The first improvements in behaviors did not tend to precede the first improvements in emotions, likely because fewer behavioral gains were found. The finding that cognitive variables tend to improve around the same time as sad mood may explain why many studies failed to find that cognitive change predicts later change in depressive symptoms.

Keywords: Experience sampling method, Major Depressive Disorder, time-series analysis, temporal precedence, process of change, sudden gains.

Abstract

The Routledge International Handbook of Changes in Human Perceptions and Behaviors is the first edited volume to present multidisciplinary perspectives on various aspects of changes that humans experience.The handbook is designed to highlight the different contents, types, ways, meanings, applications, and moments of changes that have been recognized by experts in various fields within the life and social sciences. Comprised of four sections, the chapters address changes in a variety of contexts related to human perceptions and behaviors; the moment of change and fluctuations; changes in applied settings; and the meaning of changes, including resistance to change. Written by a range of expert international contributors, the book brings together discussions and insights about how different levels and types of changes in human perceptions, attitudes, beliefs, values, and behaviors have been studied and considered in diverse fields. It also explores the various mechanisms that account for changes, exploring how and when changes occur and what changes mean to humans.Relevant for empirical and theoretical work, the handbook will be of great interest to researchers, academics and postgraduate students across psychology, behavioral sciences and social sciences.

In this chapter, we derive an understanding of mental problems and disorders from the ordinary language concepts of states and traits. More specifically, we show that mental problems are components of emotional states, that these problem-comprising emotional states are multiple and cluster in syndromes, that mental problems and the states they belong to are enduring and repetitive, and – based on the notion that goals and beliefs are clusters of dispositions – that the syndromes of problem-comprising states are characterized by basic beliefs and goals. Next, we show that cognitive-behavioral therapy (CBT) models are consistent with the developed perspective. These models refine the diagnostic descriptions of problem-comprising states and syndromes and specify changeable relationships between the state components and between the states. Then, the mechanisms of change of problem-comprising states are determined. The developed structure of psychopathology is easily translated to a dynamic system language, giving access to theories and methodologies from the system approaches. Finally, three of these approaches are discussed: use of differential calculus to formulate, simulate, and test clinical models; use of ecological momentary assessment (EMA) to derive the internal structure of states and syndromes; and use of EMA to detect early warning signals for critical transitions.

Abstract

BACKGROUND: The path to depressive symptom improvement during therapy is often complex, as many individuals experience periods of instability and discontinuous symptom change. If the process of remission follows complex dynamic systems principles, early warning signals (EWS) may precede such depressive symptom transitions.

AIMS: We aimed to test whether EWS, in the form of rises in lag-1 autocorrelation and variance, occur in momentary affect time series preceding transitions towards lower levels of depressive symptoms during therapy. We also investigated the presence of EWS in patients without symptom transitions.

METHODS: In a sample of 41 depressed individuals who were starting psychological treatment, positive affect and negative affect (high and low arousal) were measured five times a day using ecological momentary assessments (EMA) for four months (521 observations per individual on average; yielding 25,197 observations in total), and depressive symptoms were assessed weekly over six months. We used a moving window method and time-varying autoregressive generalized additive modeling (TV-AR GAM) to determine whether EWS occurred in these momentary affect measures, within-persons.

RESULTS: For the moving-window autocorrelation, 89% of individuals with transitions showed at least one EWS in one of the variables (versus 62.5% in the no-transition group), and the proportion of EWS in the separate variables was consistently higher (~44% across affect measures) than for individuals without transitions (~27%). Rising variance was found for few individuals, both preceding transitions (~11%) and for individuals without a transition (~12%).

CONCLUSIONS: The process of symptom remission showed critical slowing down in at least part of our sample. Our findings indicate that EWS are not generic across all affect measures and may have limited value as a personalized prediction method.

Keywords: critical slowing down; depression; idiographic change; replicated single-subject design; depressive symptom improvement; early warning signals; critical transitions

Abstract

Clinical research and practice are permeated by complexity. In recent years, researchers have witnessed a sharp increase in scientific studies that acknowledge this complexity and approach psychopathology from the perspective of complex systems. In this chapter, the authors provide an introduction to the complex systems approaches that have received the most attention in psychopathology. They begin by discussing the general idea of complexity and complex systems and then turn to the framework of complex dynamic system models and how they have been applied to psychopathology. After this, the authors discuss early warning signals, which hold the promise of using insights from complexity science to enable personalized prediction and timely intervention. Finally, the authors go through the recently popular network approach to psychopathology and how it is related to the broader framework of complex systems. Throughout the chapter, the authors discuss the applications, challenges, and limitations of these approaches.

Keywords: complexity, dynamical system, network, centrality, early warning signal, experience sampling method

Abstract

More instability (MSSD) and variability (SD) of negative affect (NA) have been related to current and future depressive symptoms. We investigated whether NA instability and variability were predictive of the rate of symptom improvement during treatment and of reaching remission status. Forty-six individuals with major depressive disorder completed six days of ecological momentary assessments (10 beeps/day) before starting a combination of pharmacotherapy and supportive therapy. During and after treatment, the Hamilton Depression Rating Scale (HDRS) diagnostic interview was performed monthly for 18 months. The rate of change in HDRS scores over five months (during treatment) and remission status (HDRS ≤ 7) over eighteen months were predicted by the SD, MSSD and Mean of NA, and relevant baseline predictors. Higher Mean NA and baseline symptom severity, but not NA variability or instability, predicted stronger depressive symptom reduction during treatment. Higher odds of remitting were only associated with lower Mean NA. Higher mean NA may indicate more room for improvement and thus for a steeper rate of symptom change, while lower mean NA levels may imply that remission status is more readily reached. To resolve the inconclusive findings for instability and variability as predictors of symptom improvement replication in larger samples is required.

Keywords: emotion dynamics; depression; ecological momentary assessment; symptom improvement; treatment response; remission; destabilization; instability; variability; negative affect

Abstract

Empirical evidence is mounting that monitoring momentary experiences for the presence of early warning signals (EWS) may allow for personalized predictions of meaningful symptom shifts in psychopathology. Studies aiming to detect EWS require intensive longitudinal measurement designs that center on individuals undergoing change. We recommend that researchers: (a) define criteria for relevant symptom shifts a priori to allow specific hypothesis testing; (b) balance the observation period length and high-frequency measurements with participant burden by testing ambitious designs with pilot studies; (c) choose variables that are meaningful to their patient group and facilitate replication by others. Thoroughly considered designs are necessary to assess the promise of EWS as a clinical tool to detect, prevent or encourage impending symptom changes in psychopathology.

Keywords: Psychopathology, ecological momentary assessment, symptom change, early warning signals, critical transitions

Abstract

OBJECTIVE: We examined individual overall trajectories of change and the occurrence of sudden gains in daily self-rated problem severity and the relation of these patterns to treatment response. 

METHOD: Mood disorder patients (N = 329, mean age = 44, 55% women) completed daily self-ratings about the severity of their complaints as a standard part of treatment, using the Therapy Process Questionnaire (TPQ). Per individual, the best-fitting defined (linear, log-linear, 1-step) trajectory was tested for significance: for change over time, and for specificity of the best-fitting trajectory. Two-hundred and three cases had ICD-10 Symptom Rating (ISR) depression scores posttreatment: a score ≤1 identified 114 treatment responders. Relation to response was examined for sudden gains and type of change trajectory. 

RESULTS: 138 cases (42%) had a significant decrease in problem severity, of which 54 cases (16%) had a defined trajectory: 50 cases with one-step improvement, and 4 with a linear improvement in daily problem severity. Sudden gains occurred in 28% of the total sample, and within 58% of improvement patterns. Specifically, sudden gains occurred in 68% of significant 1-step trajectories and 25% of the linear cases. Sudden gains and nonspecific change trajectories were significantly more frequent for treatment responders. 

CONCLUSIONS: At the day-level, patterns of improvement are nonlinear for most patients. Sudden gains occur within various forms of overall change and are associated with treatment response. Clinically relevant improvements in depression occur both gradually and abruptly, and this finding allows for the possibility that the remission process functions according to dynamical systems principles. 

Keywords: depression; treatment response; idiographic change pattern; sudden gains; daily assessment 

Abstract

Whereas sudden gains and losses (large shifts in symptom severity) in patients receiving psychotherapy appear abrupt and hence may seem unexpected, hypotheses from complex-systems theory suggest that sudden gains and losses are actually preceded by certain early-warning signals (EWSs). We tested whether EWSs in patients’ daily self-ratings of the psychotherapeutic process predicted future sudden gains and losses. Data were collected from 328 patients receiving psychotherapy for mood disorders who completed daily self-ratings about their therapeutic process using the Therapy Process Questionnaire (TPQ). Sudden gains and losses were classified from the Problem Intensity scale of the TPQ. The other items of the TPQ were used to compute the EWSs. EWSs predicted an increased probability for sudden gains and losses in a 4-day predictive window. These results show that EWSs can be used for real-time prediction of sudden gains and losses in clinical practice. 

Keywords: early-warning signals; sudden gains; mood disorders; complex systems; psychotherapy; open materials; preregistered 

Abstract

Objective: The present study aimed to study the prevalence of sudden gains in a large sample of patients with complex anxiety disorders, experiencing long-lasting and treatment-resistant anxiety. In addition, the properties of these gains were explored in two treatment modalities: Cognitive Behavioral Therapy (CBT) and Metacognitive Therapy (MCT).

Method: The participants were 404 adults with a primary diagnosis of anxiety disorder, receiving 8-12 weeks of inpatient treatment with CBT or MCT. Symptom severity was measured weekly with the Beck Anxiety Inventory (BAI) and additionally during the first assessment session, pre-treatment, post-treatment, and at one-year follow-up. Sudden gains were identified as a between-session symptom change of ≥ 9 points on the BAI, which were also relatively large and stable compared to the weeks before and after the gain.

Results: The prevalence of sudden gains in anxiety symptoms during treatment was 19%. Individuals experiencing sudden gains had significantly lower levels of anxiety immediately after treatment and at one-year follow-up compared to those without. Sudden gains were present in both CBT and MCT, showing similar magnitudes and prevalence, but patients treated with MCT tended to experience sudden gains earlier than those with CBT. 

Conclusions: This study extends the concept of sudden gains to a long-lasting and treatment-resistant anxiety disorder population treated with CBT and MCT. The occurrence of sudden gains is predictive of both short- and long-term outcomes in reduced symptom levels. Further research is needed to reveal what initiates the gains and the characteristics of these patients.

Keywords: sudden gains, complex anxiety disorders, Cognitive Behavioral Therapy, Metacognitive therapy, treatment outcome

Abstract

Background: There is a lack of knowledge on the significance of anxious distress in patients diagnosed with depression. The aim of this study is to improve insight into the association between experienced anxious distress and daily life level of activity and specific activities (i.e., social and physical activities) in depressed individuals. 

Methods: Participants were patients with mild to severe levels of depressive symptoms (N=54). Anxious distress was assessed at baseline with the SCL-90 questionnaire and active behaviour in daily life was assessed using Ecological Momentary Assessments (EMA). Using one week of EMA data, we tested the association between level of anxiety and four operationalizations of momentary active behaviours: ‘number of activities, ‘perceived physical activity’, ‘perceived social interaction, ‘social activity’ using multilevel analyses. We also explored the association between anxiety and more specific activities.

Results: Level of anxiety was not associated with any of the four operationalizations of behaviours. However, when specific activities were predicted, anxiety was positively associated with the activity ‘household, groceries, administration’ and negatively associated with the activity ‘sports, walking, cycling’. 

Conclusions: Anxious distress in depressed individuals is not associated with general level of active behaviours, but it is associated with some specific behaviours. Thus, for clinical practice, it is more useful to assess specific behaviours instead of grouped types of behaviour in patients suffering from anxious distress.  

Keywords: experience sampling method, depression, anxiety, avoidance behaviour, social interaction, behavioural activation 

Abstract

The ubiquity of digital technologies has increased the assessment of individuals' thoughts, behaviors, and experiences via electronic devices. Surveys on smartphones or laptops often implement Visual Analogue Scales (VAS), recording responses on a continuous slider from 0 to 100. This is particularly relevant for data collection in daily life, such as ecological momentary assessments (EMA), repeatedly presenting items on mobile devices. However, the accuracy of digital VAS has been questioned, particularly regarding tactile precision (e.g., finger-to-screen size ratio), the consistency of scale interpretation between respondents, and over time (e.g., reactivity to repeated measurement). Participants (N = 3,761, 67.03% female; Mage = 47.09; SD = 14.41) from the Critical Incidents and Psychological Adaptation (CIPA) Study completed a 30-day EMA assessment. We investigated the accuracy of VAS in terms of (1) tactile precision, and (2) respondents’ perception of the neutral point post-EMA, and (3) test-retest consistency of affect ratings pre- and post-EMA. (1) Tactile precision was assessed by asking participants to enter exactly 31 on a 0–100 slider. Results showed high precision (M = 31.01; SD = 3.28; 87.0% scored between 30-32). (2) The neutral point was assessed by asking participants to indicate the score for feeling neutral on two affect items (unipolar and bipolar), with 82.19% and 88.89% indicating the scale midpoint (50 and 0, ± 5) as neutral point respectively. Neutral points deviating from the midpoint were strongly correlated (r = .71–.73) with the person-specific means across the EMA period on the respective item. (3) Test-retest consistency was evaluated by asking participants to rate how happy/sad, they/others would be in response to affective events (e.g., a serious argument) pre- and post-EMA. Consistency across time was high (median change = 0–5). Findings support the accuracy and consistency of digital VAS and alleviate several key concerns about potential inaccuracy.

Keywords: digital assessment, ecological momentary assessment (EMA), measurement accuracy, reactivity, scale interpretation, self-report

Abstract

As the global urban population surpasses 50%, understanding the impact of urban environments on mental health is crucial. This study examines the relationship between urbanicity and the prevalence of depression and anxiety disorders in the United Kingdom (UK; N = 449,323), New Zealand (N = 33,042), and Norway (N = 13,238). We address the limitations of previous research, which relied on dichotomous and varying definitions of urbanicity, by employing a continuous measure of urbanicity consistently across our three samples. Results revealed country-specific patterns: in the UK, a non-linear pattern showed a minimum in semi-urban areas but increased prevalences of anxiety and depression in both urban and rural regions; in New Zealand, only urban living was linked to a higher prevalence of anxiety, while depression rates remained consistent across settings; in Norway, increased mental health problems were associated with rural residency. These relationships were robust across various sensitivity analyses. The results underscore the country-specific connection between urbanicity and mental health, offering a path for future research to explore cross-national variation in urban environments toward identifying aspects of urban life that serve as favorable versus detrimental to mental health.

Keywords: anxiety; depression; rural; cities; urban mental health 

Brief background:

Early Warning Signals (EWS) are theoretical indicators of critical slowing down and low resilience in a complex dynamical system, where a rise in EWS indicates that the likelihood of an upcoming critical transition is higher. EWS have shown promise in anticipating large shifts in depressive symptoms in a few studies already. However, no study so far has tested whether rises in autocorrelation at lag-1 (AR(1)) and variance (SD) can be found within-persons prior to depressive symptom improvement.

Examining this is clinically and theoretically important, as it could provide patient and therapist valuable insight into whether the system is sensitive to positive changes (is destabilizing), and may be close to recovery, even if it may not be overtly noticeable in the symptoms yet. Moreover, it takes the next step from viewing mental disorder from a complex systems angle, to actually testing whether the principles of dynamical systems apply to transitions in psychological systems.

Research question:

Can we find indicators of critical slowing down (CSD) in the form of early warning signals (EWS) like increases in AR(1) and variance before transitions towards improvement in depressive symptoms?

Hypotheses: 

• Does the autoregressive coefficient at lag-1 (AR(1)), in moment-to-moment mood assessments, increase before a sudden shift in depressive symptoms on the SCL-90?

• Does variability (SD), in momentary mood measurements, increase before a sudden shift in depressive symptoms on the SCL-90?

Description

AIM: The Transitions in Depression (TRANS-ID) Recovery study has gathered intensive longitudinal data in a group of individuals with depression during psychological therapy. The study was designed to gather high-resolution time series to monitor individual change processes in great detail, to allow for personalized predictions of shifts in depressive symptoms.

METHOD: The data collection combined experience sampling methods to assess momentary affect and behavior (five 27-item questionnaires a day, for four months), ambulatory assessment of physical activity and heart rate (continuous, for four months), and depression symptom assessments (weekly for six months, and monthly for the six months thereafter, twelve months total). In addition to a baseline diagnostic interview, baseline questionnaires covered a range of constructs, including overall psychopathology symptoms, medication use, psychological treatment history, alexithymia, life events, quality of life, and chronotype. After the four-month ambulatory assessment period, a personal report of the experience sampling data was given to each participant, and a semi-structured qualitative interview was conducted to evaluate participants’ own retrospective experience of symptom changes during the research period.

CONCLUSION: The TRANS-ID Recovery study procedures and materials are described in detail in this document. The study protocol was approved by the Medical Ethical Committee of the University Medical Center Groningen (reg. number: NL58848.04.16).

Brief background:

When a dynamical system approaches a tipping point, the stabilizing forces tend to decrease and the rate at which the system can recover from perturbations slows down (a phenomenon known as Critical Slowing Down). Early Warning Signals (EWS) are theoretical indicators of Critical Slowing Down and low resilience in a complex dynamical system, where a rise in EWS indicates that the likelihood of an upcoming critical transition is higher.

Two earlier studies by Wichers and colleagues have demonstrated the potential of EWS for anticipating large shifts from a low level of depressive symptoms towards a higher level of depressive symptoms in individual patients. We call such shifts ‘transitions in depression’. In these studies, mood was measured prospectively using the Experience Sampling Method (ESM), multiple times a day over the course of several months, starting when depression was still in remission, and continuing until after a large increase in depressive symptoms occurred. This data was uniquely able to test for the presence of EWS in psychology, as it was the first to capture both the transition in depression itself and the period that led up to it with intensive longitudinal data. As hypothesized, a rise in EWS was found at the within-person level for both patients that experienced a transition in depression; such rises were absent in the majority of the five patients that did not experience a transition.

As only a total of seven participants divided over two studies have been tested so far, there is a pressing need to investigate if EWS can indeed anticipate transitions in depression in a much larger sample. In the current study, similar intensive longitudinal data has been obtained for 56 participants. The aim is to test if lag-1 autocorrelation and variance in ESM time series increase before these transitions occur, and if these same EWS are absent in participants who did not experience a transition in depression. Transitions in depression were defined for this sample in a previous study.

Research question:

Can we find Early Warning Signals (EWS) that indicate Critical Slowing Down in the form of lag-1 autocorrelation and variance in ESM data obtained in the period leading up to transitions towards higher levels of depressive symptoms?

Hypotheses: 

• Lag-1 autocorrelation and variance of ESM mood scores obtained before transitions towards a higher level of depressive symptoms will show an increase that is both statistically significant and larger than a minimum effect size of interest (tau > .1).

• In patients who did not experience a transition in depressive symptoms, rises in lag-1 autocorrelation and variance that are both relevant and significant will be found less frequently than in patients who experienced a transition.

• The average magnitude of the trend in lag-1 autocorrelation and variance will be higher in the group of participants that experienced a transition in depression than in the group of participants that did not experience a transition in depression.

Description

AIM: The Transitions in Depression (TRANS-ID) Tapering study has gathered intensive longitudinal data in a group of individuals who were previously prescribed antidepressant medication for depressive symptoms, which they tapered during the study period. The study was designed to gather high-resolution time series to monitor individual change processes in great detail, to allow for personalized predictions of shifts in depressive symptoms.

METHOD: The data collection combined experience sampling methods to assess momentary affect and behavior (five 28-item questionnaires a day, for four months), ambulatory assessment of physical activity and heart rate (continuous, for four months), and depression symptom assessments (weekly for six months). In addition to a baseline diagnostic interview, baseline questionnaires covered a range of constructs, including overall psychopathology symptoms, medication use, psychological treatment history, alexithymia, life events, quality of life, and chronotype. After the four-month ambulatory assessment period, a personal report of the experience sampling data was given to each participant, and a semi-structured qualitative interview was conducted to evaluate participants’ own retrospective experience of symptom changes during the research period.

CONCLUSION: The TRANS-ID Tapering study procedures and materials are described in detail in this document. The study protocol was approved by the Medical Ethical Committee of the University Medical Center Groningen (ABR no. NL58469.042.16).