Sudden gains in day-to-day change: Revealing nonlinear patterns of individual improvement in depression.

Helmich, Marieke A.; Wichers, Marieke; Olthof, Merlijn; Strunk, Guido; Aas, Benjamin; Aichhorn, Wolfgang; Schiepek, Günter; Snippe, Evelien

doi:10.1037/ccp0000469

Cited by 50 publications

(46 citation statements)

References 62 publications

(115 reference statements)

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“…We showed that, on average, we need sample sizes approaching n = 89 for single-subject VAR models to outperform AR models. While this may seem like a relatively large sample size requirement, such longer time series are becoming more common in psychological research [ 19 , 20 ] Decomposing this variance showed that (i) one cannot expect reliable statements with respect to the relative performance of the AR and VAR models that ignore the characteristics of the generating model, and (ii) that choosing reliably between AR and VAR models is difficult for most sample sizes typically available in psychological research. Finally, we provided a theoretical explanation for when the “1 Standard Error Rule” outperforms simply selecting the model with lowest prediction error, and showed that the 1SER performs better when n is small.…”

Section: Discussionmentioning

confidence: 99%

Choosing between AR(1) and VAR(1) models in typical psychological applications

2020

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Time series of individual subjects have become a common data type in psychological research. The Vector Autoregressive (VAR) model, which predicts each variable by all variables including itself at previous time points, has become a popular modeling choice for these data. However, the number of observations in typical psychological applications is often small, which puts the reliability of VAR coefficients into question. In such situations it is possible that the simpler AR model, which only predicts each variable by itself at previous time points, is more appropriate. Bulteel et al. (2018) used empirical data to investigate in which situations the AR or VAR models are more appropriate and suggest a rule to choose between the two models in practice. We provide an extended analysis of these issues using a simulation study. This allows us to (1) directly investigate the relative performance of AR and VAR models in typical psychological applications, (2) show how the relative performance depends both on n and characteristics of the true model, (3) quantify the uncertainty in selecting between the two models, and (4) assess the relative performance of different model selection strategies. We thereby provide a more complete picture for applied researchers about when the VAR model is appropriate in typical psychological applications, and how to select between AR and VAR models in practice.

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Section: Discussionmentioning

confidence: 99%

Choosing between AR(1) and VAR(1) models in typical psychological applications

2020

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“…The study of these regime shifts, or broader defined, phase transitions, is an important avenue for clinical science. Scholars have argued that many important clinical events such as the onset of psychopathology [48], relapse [49], suicide attempts [50], clinical improvement [51][52][53][54][55][56][57] and clinical deterioration [51,52] may be instances of phase transitions. Also, the instability that precedes such transitions can be detected as so-called early-warning signals (EWS) that may be used for shortterm prediction of clinical change [55,58].…”

Section: Introductionmentioning

confidence: 99%

Complexity in psychological self-ratings: implications for research and practice

Olthof

Hasselman

Lichtwarck‐Aschoff

2020

BMC Med

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Background Psychopathology research is changing focus from group-based “disease models” to a personalized approach inspired by complex systems theories. This approach, which has already produced novel and valuable insights into the complex nature of psychopathology, often relies on repeated self-ratings of individual patients. So far, it has been unknown whether such self-ratings, the presumed observables of the individual patient as a complex system, actually display complex dynamics. We examine this basic assumption of a complex systems approach to psychopathology by testing repeated self-ratings for three markers of complexity: memory, the presence of (time-varying) short- and long-range temporal correlations; regime shifts, transitions between different dynamic regimes; and sensitive dependence on initial conditions, also known as the “butterfly effect,” the divergence of initially similar trajectories. Methods We analyzed repeated self-ratings (1476 time points) from a single patient for the three markers of complexity using Bartels rank test, (partial) autocorrelation functions, time-varying autoregression, a non-stationarity test, change point analysis, and the Sugihara-May algorithm. Results Self-ratings concerning psychological states (e.g., the item “I feel down”) exhibited all complexity markers: time-varying short- and long-term memory, multiple regime shifts, and sensitive dependence on initial conditions. Unexpectedly, self-ratings concerning physical sensations (e.g., the item “I am hungry”) exhibited less complex dynamics and their behavior was more similar to random variables. Conclusions Psychological self-ratings display complex dynamics. The presence of complexity in repeated self-ratings means that we have to acknowledge that (1) repeated self-ratings yield a complex pattern of data and not a set of (nearly) independent data points, (2) humans are “moving targets” whose self-ratings display non-stationary change processes including regime shifts, and (3) long-term prediction of individual trajectories may be fundamentally impossible. These findings point to a limitation of popular statistical time series models whose assumptions are violated by the presence of these complexity markers. We conclude that a complex systems approach to mental health should appreciate complexity as a fundamental aspect of psychopathology research by adopting the models and methods of complexity science. Promising first steps in this direction, such as research on real-time process monitoring, short-term prediction, and just-in-time interventions, are discussed.

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“…Importantly, it has been shown that such discontinuities have a clinical impact. For example, Helmich et al (2020) report that sudden gains and nonlinear trajectories of the therapeutic progress were significantly more frequently observed in treatment responders. Likewise, increasing fluctuations have repeatedly been shown to improve treatment outcome as they indicate possibilities for the patient to reorganize (Schiepek et al, 2020).…”

Section: Implications For Clinical Practice and Researchmentioning

confidence: 99%

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

et al. 2020

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In many cases, the dynamics of psychotherapeutic change processes is characterized by sudden and critical transitions. In theoretical terms, these transitions may be "phase transitions" of self-organizing nonlinear systems. Meanwhile, a variety of methods is available to identify phase transitions even in short time series. However, it is still an open question if different methods for timeseries analysis reveal convergent results indicating the moments of critical transitions and related precursors. Methods and Procedures: Seven concepts which are commonly used in nonlinear time series analysis were investigated in terms of their ability to identify changes in psychological time series: Recurrence Plots, Change Point Analysis, Dynamic Complexity, Permutation Entropy, Time Frequency Distributions, Instantaneous Frequency, and Synchronization Pattern Analysis, i.e., the dynamic inter-correlation of the system's variables. Phase transitions were simulated by shifting control parameters in the Hénon map dynamics, in a simulation model of psychotherapy processes (one by an external shift of the control parameter and one created by a simulated control parameter shift), and three sets of empirical time series generated by daily self-ratings of patients during the treatment. Results: The applied methods showed converging results indicating the moments of dynamic transitions within an acceptable tolerance. The convergence of change points was confirmed statistically by a comparison to random surrogates. In the three simulated dynamics with known phase transitions, these could be identified, and in the empirical cases, the methods converged indicating one and the same transition (possibly the phase transitions of the cases). Moreover, changes that did not manifest in a shift of mean or variance could be detected.

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Sudden gains in day-to-day change: Revealing nonlinear patterns of individual improvement in depression.

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Cited by 50 publications

References 62 publications

Choosing between AR(1) and VAR(1) models in typical psychological applications

Choosing between AR(1) and VAR(1) models in typical psychological applications

Complexity in psychological self-ratings: implications for research and practice

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

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