Filtern
Dokumenttyp
Volltext vorhanden
- ja (4) (entfernen)
Schlagworte
- Therapieabbruch (4) (entfernen)
Institut
- Fachbereich 1 (2)
- Psychologie (2)
There is no longer any doubt about the general effectiveness of psychotherapy. However, up to 40% of patients do not respond to treatment. Despite efforts to develop new treatments, overall effectiveness has not improved. Consequently, practice-oriented research has emerged to make research results more relevant to practitioners. Within this context, patient-focused research (PFR) focuses on the question of whether a particular treatment works for a specific patient. Finally, PFR gave rise to the precision mental health research movement that is trying to tailor treatments to individual patients by making data-driven and algorithm-based predictions. These predictions are intended to support therapists in their clinical decisions, such as the selection of treatment strategies and adaptation of treatment. The present work summarizes three studies that aim to generate different prediction models for treatment personalization that can be applied to practice. The goal of Study I was to develop a model for dropout prediction using data assessed prior to the first session (N = 2543). The usefulness of various machine learning (ML) algorithms and ensembles was assessed. The best model was an ensemble utilizing random forest and nearest neighbor modeling. It significantly outperformed generalized linear modeling, correctly identifying 63.4% of all cases and uncovering seven key predictors. The findings illustrated the potential of ML to enhance dropout predictions, but also highlighted that not all ML algorithms are equally suitable for this purpose. Study II utilized Study I’s findings to enhance the prediction of dropout rates. Data from the initial two sessions and observer ratings of therapist interventions and skills were employed to develop a model using an elastic net (EN) algorithm. The findings demonstrated that the model was significantly more effective at predicting dropout when using observer ratings with a Cohen’s d of up to .65 and more effective than the model in Study I, despite the smaller sample (N = 259). These results indicated that generating models could be improved by employing various data sources, which provide better foundations for model development. Finally, Study III generated a model to predict therapy outcome after a sudden gain (SG) in order to identify crucial predictors of the upward spiral. EN was used to generate the model using data from 794 cases that experienced a SG. A control group of the same size was also used to quantify and relativize the identified predictors by their general influence on therapy outcomes. The results indicated that there are seven key predictors that have varying effect sizes on therapy outcome, with Cohen's d ranging from 1.08 to 12.48. The findings suggested that a directive approach is more likely to lead to better outcomes after an SG, and that alliance ruptures can be effectively compensated for. However, these effects
were reversed in the control group. The results of the three studies are discussed regarding their usefulness to support clinical decision-making and their implications for the implementation of precision mental health.
Internet interventions have gained popularity and the idea is to use them to increase the availability of psychological treatment. Research suggests that internet interventions are effective for a number of psychological disorders with effect sizes comparable to those found in face-to-face treatment. However, when provided as an add-on to treatment as usual, internet interventions do not seem to provide additional benefit. Furthermore, adherence and dropout rates vary greatly between studies, limiting the generalizability of the findings. This underlines the need to further investigate differences between internet interventions, participating patients, and their usage of interventions. A stronger focus on the processes of change seems necessary to better understand the varying findings regarding outcome, adherence and dropout in internet interventions. Thus, the aim of this dissertation was to investigate change processes in internet interventions and the factors that impact treatment response. This could help to identify important variables that should be considered in research on internet interventions as well as in clinical settings that make use of internet interventions.
Study I (Chapter 5) investigated early change patterns in participants of an internet intervention targeting depression. Data from 409 participants were analyzed using Growth Mixture Modeling. Specifically a piecewise model was applied to model change from screening to registration (pretreatment) and early change (registration to week four of treatment). Three early change patterns were identified; two were characterized by improvement and one by deterioration. The patterns were predictive of treatment outcome. The results therefore indicated that early change should be closely monitored in internet interventions, as early change may be an important indicator of treatment outcome.
Study II (Chapter 6) picked up on the idea of analyzing change patterns in internet interventions and extended it by using the Muthen-Roy model to identify change-dropout patterns. A sligthly bigger sample of the dataset from Study I was analyzed (N = 483). Four change-dropout patterns emerged; high risk of dropout was associated with rapid improvement and deterioration. These findings indicate that clinicians should consider how dropout may depend on patient characteristics as well as symptom change, as dropout is associated with both deterioration and a good enough dosage of treatment.
Study III (Chapter 7) compared adherence and outcome in different participant groups and investigated the impact of adherence to treatment components on treatment outcome in an internet intervention targeting anxiety symptoms. 50 outpatient participants waiting for face- to-face treatment and 37 self-referred participants were compared regarding adherence to treatment components and outcome. In addition, outpatient participants were compared to a matched sample of outpatients, who had no access to the internet intervention during the waiting period. Adherence to treatment components was investigated as a predictor of treatment outcome. Results suggested that especially adherence may vary depending on participant group. Also using specific measures of adherence such as adherence to treatment components may be crucial to detect change mechanisms in internet interventions. Fostering adherence to treatment components in participants may increase the effectiveness of internet interventions.
Results of the three studies are discussed and general conclusions are drawn.
Implications for future research as well as their utility for clinical practice and decision- making are presented.
Die patienten-fokussierte Psychotherapieforschung hat das Ziel, den Erfolg von Psychotherapie durch die kontinuierliche Messung und Rückmeldung von Prozessvariablen zu verbessern. Es konnte bereits gezeigt werden, dass nicht nur Patienten-spezifische Charakterisitika, wie die Symptomreduktion, sondern auch dyadische Merkmale, wie die therapeutische Beziehung, indikativ sind. Ein vielversprechender neuer Ansatz bzgl. der Messung dyadischer Charakteristika ist nonverbale Synchronie, die definiert ist als Bewegungskoordination zwischen Interaktionspartnern. Nonverbale Synchronie kann inzwischen objektiv und automatisch in Therapievidoes gemessen werden, was die Methodik frei von Biases wie selektiver Wahrnehmung oder sozialer Erwünschtheit macht. Frühe Studien aus der Sozial- und Entwicklungspsychologie konnten Zusammenhänge mit sozialer Bindung und Sympathie finden. Erste Studien aus der Psychotherapieforschung weisen auf Zusammenhänge zwischen nonverbaler Synchronie und der Therapiebeziehung sowie dem Therapieerfolg hin und geben erste Hinweise darauf, dass nonverbale Synchronie eine zusätzliche Informationsquelle für dyadische Aspekte sein kann, mit der man zukünftig frühzeitig Therapieerfolge vorhersagen könnte. Die vorliegende Arbeit beinhaltet drei Studien zu nonverbaler Synchronie in der ambulanten Psychotherapie und Zusammenhängen mit therapeutischen Prozessen. In Studie 1 wurde nonverbale Synchronie in einer diagnose-heterogenen Stichprobe von N=143 Patienten zu Therapiebeginn gemessen. Mittels Mehrebenenanalysen konnte die Validität der Messmethodik bestätigt werden. Des weiteren wurden Zusammenhänge mit bestimmten Artes des Therapieerfolgs gefunden: Patienten, die unverändert die Therapie abbrachen zeigten das niedrigste Level an Synchronie, während Patienten, die unverändert die Therapie zu Ende führten das höchste Level hatte und Patienten mit einer reliablen Symptomreduktion ein mittleres Level an nonverbaler Synchrony aufwiesen (auch unter Kontrolle der Therapiebeziehung). In Studie 2 wurden nonverbale Synchronie und die Bewegungsmenge zu Therapiebeginn und zum Therapieende erfasst und in zwei Stichproben von Patienten mit Depression (N=68) und Patienten mit Angststörungen (N=25) verglichen. Mehrebenenanalysen zeigten weniger Bewegungsmenge und Synchronie bei Dyaden mit depressiven Patienten, wobei sich beide Gruppen zum Therapieende nicht mehr in der nonverbalen Synchronie unterschieden. In Studie 3 wurde nonverbale Synchronie in einer Stichprobe von N=111 Patienten mit Sozialer Phobie zu vier Zeitpunkten im Therapieverlauf gemessen (N=346 Videos). Mehrebenenanalysen zeigten einen kontinuierlich sinkenden Verlauf der Synchronie und einen Moderationseffekt auf den Zusammenhang zwischen frühen Verbesserungen und dem Therapieerfolg.
There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients" dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.