Predicting personalized process-outcome associations in psychotherapy using machine learning approaches—A demonstration

Rubel, Julian; Zilcha‐Mano, Sigal; Giesemann, Julia; Prinz, Jessica; Lutz, Wolfgang

doi:10.1080/10503307.2019.1597994

Cited by 42 publications

(39 citation statements)

References 33 publications

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“…In the current analysis, trajectories were compared within the time series of the same person, but the butterfly effect applies equally well to trajectories of different persons. In a recent study by Rubel et al [88], process-outcome relations in psychotherapy could not be reliably predicted on the basis of processoutcome relations from patients with highly similar pretreatment characteristics. A finding which may be explained by the presence of a butterfly effect (i.e., individual trajectories diverge over time; see also [61,62]).…”

Section: Discussionmentioning

confidence: 91%

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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Section: Discussionmentioning

confidence: 91%

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

Olthof

Hasselman

Lichtwarck‐Aschoff

2020

BMC Med

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“…More specifically to the interest of this study, Rubel et al (2019), using machine learning methods, found general interpersonal distress together with ten other baseline predictors (out of 95 possible) to moderate the within-patient alliance effect on outcome.…”

Section: Interpersonal Agency As Predictor Of the Within-patient Allimentioning

confidence: 86%

Interpersonal agency as predictor of the within-patient alliance effects on depression severity.

Babl¹,

Krieger²,

Heinonen³

et al. 2020

Journal of Consulting and Clinical Psychology

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Aim. Several studies have reported significant within-patient effects of the therapeutic alliance on outcome. However, it remains uncertain whether there are specific patient groups for whom an improved alliance might be particularly beneficial. The relational nature of the alliance makes patients' interpersonal problems a promising candidate for examining such differential effects. This study aims to analyze (i) between-and within-patient effects of the alliance on depression severity, (ii) the effects of patients' baseline interpersonal problems on the within-patient alliance-outcome association, and (iii) whether within-patient effects of the alliance remain significant when adjusting for patients' initial interpersonal problems. Methods. A sample of 141 patients with depression and undergoing outpatient psychotherapy completed the Inventory of Interpersonal Problems at baseline as well as a brief version of the Working Alliance Inventory and the Well-Being Index (as a proxy of depression severity) session-by-session. Results. Multilevel models revealed both significant between-and withinpatient effects of the alliance on improvements in depression severity. Patients' problems relating to agency had a significant effect on the within-patient effect of alliance, with more submissive patients benefiting more from an improved therapeutic alliance. Finally, the between-and within-patient effects of the alliance remained significant when adjusting for the patients' agentic interpersonal problems and treatment condition. Conclusions. The results provide evidence on which types of patients would particularly benefit from an improved therapeutic alliance. For patients suffering from low interpersonal agency and reporting problems with submissiveness, an enhanced alliance during the therapy process might improve treatment outcome.

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“…Arguments can be found in the literature for emphasizing the importance of person-specific analyses to achieve a more nuanced understanding of the diversity of intra-individual patterns (e.g., Barlow & Nock, 2009). In the clinical realm, some have begun to collect quantitative data as a strategy to better understand treatment processes (Rubel, Zilcha-Mano, Giesemann, Prinz, & Lutz, 2019;Brown, Bosley, Kenyon, Chen, & Levenson, 2019). More recently, empirical and theoretical papers have discussed the merits of idiographic data collection and analysis applied to clinical work (Fisher, 2015;Piccirillo, Beck, & Rodebaugh, 2019).…”

Section: Introductionmentioning

confidence: 99%

Evidence for the Feasibility of Person-Specific Ecological Momentary Assessment Across Diverse Populations and Study Designs

Soyster¹,

Bosley²,

Reeves³

et al. 2019

JPOR

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Clinical psychological science has seen an exciting shift toward the use of person-specific (idiographic) approaches to studying psychopathology and change in treatment at the level of the individual. One commonly used method in idiographic research is ecological momentary assessment (EMA). EMA offers a way to sample individuals intensivelyoften multiple times per dayas they go about their lives. While these methods offer benefits such as greater ecological validity and streamlined data collection, many share concerns about their feasibility across diverse clinical populations. To investigate the feasibility of using EMA to study psychological processes idiographically both in-and out of the context of therapy, the present study aggregated participants across seven studies spanning diverse clinical and community populations (N = 496), all of which utilized an idiographic EMA approach to study symptoms of psychopathology (e.g., PTSD, mood and anxiety, substance abuse). In a series of linear regression models, participant and study design characteristics were used to predict compliance with EMA surveys. Across study designs, we found that (1) participants were willing to report on symptoms and mechanisms relating to a wide range of psychopathological domains; (2) on average, participants completed 82.21% (SD = 16.34%) of all EMA surveys; and (3) compliance with EMA surveys was not significantly related to participant demographics, psychological diagnosis, personality characteristics, or most study characteristics (e.g., number of surveys per day). These findings suggest feasibility of idiographic EMA for collecting the data needed to understand psychopathology and change in treatment at the level of the individual.

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Predicting personalized process-outcome associations in psychotherapy using machine learning approaches—A demonstration

Cited by 42 publications

References 33 publications

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

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

Interpersonal agency as predictor of the within-patient alliance effects on depression severity.

Evidence for the Feasibility of Person-Specific Ecological Momentary Assessment Across Diverse Populations and Study Designs

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