Tailoring the treatment to the individual patient can improve the effectiveness of psychotherapy. To do so, the treatment or strategy with the best prognosis for the individual patient can be selected at the beginning of therapy. Furthermore, the therapeutic approach can be adapted during the course of treatment. To support the therapist in such decisions, prediction algorithms are used, which are able to process complex and comprehensive data (precision mental health). Statistical methods used in psychotherapy research to analyze data and develop predictive models have recently become more advanced. However, algorithms cannot do better than what the underlying data provide them in terms of information. Therefore, our predictions and recommendations could benefit from a broader range of data beyond the traditional patient- or therapist-rated questionnaires.
The goal of this Research Topic is to investigate new measures and data assessment methods for psychotherapy research. Promising measures beyond questionnaires have to be developed, validated, and evaluated for their usefulness in the context of precision mental health for prediction and support in clinical decision-making. Interdisciplinary research and integration of assessment methods and data from other disciplines into psychotherapy research will enable the expansion of our information about patients. Recent developments in the areas of video analysis, speech analysis, neuroimaging, the study of psychophysiological parameters, the collection and analysis of movement data in patients' daily lives, and much more already foreshadow the possibilities inherent in these new levels of data for the personalization of psychotherapy. Therefore, this research topic will address these new options in assessing information about psychotherapy patients and the clinical utility of these new data.
Topics may include:
• Methods for analyzing psychotherapy video recordings (e.g., video analysis, speech analysis)
• Digital phenotyping with smartphone or other digital devices (such as smartwatches)
• Predicting early treatment response, treatment outcome or dropout using innovative measures
• Longitudinal data analysis to investigate treatment processes and process-outcome associations
• Combining psychological, neuroimaging, biological and/or physiological variables
• Developing automated assessment and analytical methods to collect data
• Validation of innovative measures with established self-report or other subjective and objective measures
Tailoring the treatment to the individual patient can improve the effectiveness of psychotherapy. To do so, the treatment or strategy with the best prognosis for the individual patient can be selected at the beginning of therapy. Furthermore, the therapeutic approach can be adapted during the course of treatment. To support the therapist in such decisions, prediction algorithms are used, which are able to process complex and comprehensive data (precision mental health). Statistical methods used in psychotherapy research to analyze data and develop predictive models have recently become more advanced. However, algorithms cannot do better than what the underlying data provide them in terms of information. Therefore, our predictions and recommendations could benefit from a broader range of data beyond the traditional patient- or therapist-rated questionnaires.
The goal of this Research Topic is to investigate new measures and data assessment methods for psychotherapy research. Promising measures beyond questionnaires have to be developed, validated, and evaluated for their usefulness in the context of precision mental health for prediction and support in clinical decision-making. Interdisciplinary research and integration of assessment methods and data from other disciplines into psychotherapy research will enable the expansion of our information about patients. Recent developments in the areas of video analysis, speech analysis, neuroimaging, the study of psychophysiological parameters, the collection and analysis of movement data in patients' daily lives, and much more already foreshadow the possibilities inherent in these new levels of data for the personalization of psychotherapy. Therefore, this research topic will address these new options in assessing information about psychotherapy patients and the clinical utility of these new data.
Topics may include:
• Methods for analyzing psychotherapy video recordings (e.g., video analysis, speech analysis)
• Digital phenotyping with smartphone or other digital devices (such as smartwatches)
• Predicting early treatment response, treatment outcome or dropout using innovative measures
• Longitudinal data analysis to investigate treatment processes and process-outcome associations
• Combining psychological, neuroimaging, biological and/or physiological variables
• Developing automated assessment and analytical methods to collect data
• Validation of innovative measures with established self-report or other subjective and objective measures