Periodic Reporting for period 2 - Personalized-PredInt (Personalized Prediction and Intervention for Behavioral Avoidance and Maladaptive Affective States)
Berichtszeitraum: 2022-10-01 bis 2023-09-30
The Personalized-PredInt research project explored the feasibility and utility of personalized prediction and intervention for maladaptive behaviors and emotional states. Generating personalized prediction models offers an intriguing avenue for prevention and intervention science. To date, however, such prediction has been limited by the heterogeneity in factors determining individuals' behaviors and emotions. To address this limitation, the project adopted an idiographic approach while focusing on a few exemplar targets that constitute transdiagnostic components in various psychological disorders. Specifically, the project aimed to (a) develop person-specific models for predicting target behaviors and emotional states; and (b) use the models to inform person-specific interventions. To do so, the project utilized ecological momentary assessment (EMA) to generate a scalable personalized system that provides accurate prediction using brief psychosocial questions and timing/location data (Study 1). In Study 2, person-specific models were used to tailor a clinician-administered single session intervention. In Study 3, person-specific models were used to inform participants in real time regarding the increased likelihood of impending targets, and prompt appropriate interventions.
The project expanded the toolbox of idiographic psychological scientists and validated novel methods to leverage intensive longitudinal data to benefit individuals facing psychological difficulties.
The project expanded the toolbox of idiographic psychological scientists and validated novel methods to leverage intensive longitudinal data to benefit individuals facing psychological difficulties.
The work performed during the outgoing phase at the UC Berkeley commenced by reviewing recent work in the field of clinical idiographic modeling. This process resulted in notable revisions to the project’s design. First, the review highlighted the importance of base rates to allow the generation of accurate, reliable, and useful prediction models. Hence, Study 1’s population was set to be clinically diagnosed socially anxious participants. Second, the review pointed out the difficulties in generating accurate models of emotional states. Hence, a specific effort to characterize compound emotional states was initialized. Third, the review drew attention to the ambiguous nature of psychological targets (e.g. avoidance), making them harder to predict and intervene upon. Hence, Study 2 has been redesigned to employ concurrent models that guide clinician-administered single-session interventions.
During Study 1's data collection, two smaller projects were launched. The first reviewed the impact of timing on affect dynamics research in a chapter titled ‘A Close Look at the Role of Time in Affect Dynamics Research’. The second analyzed ecological momentary assessment (EMA) data from an open trial, discovering that the differentiation of negative emotions predicts treatment outcomes. This finding, published in Frontiers in Psychology, illustrated the utility of pre-treatment EMA for identifying patient characteristics that forecast treatment success.
Additionally, as part of an effort to characterize emotional states, a new approach to model compound states has been developed – a Moment-Oriented Approach. It employs unsupervised clustering techniques to delineate archetypal patterns of momentary mixtures of emotions. The approach allows the identification of regularities that exist both within individuals and across the sample. Moreover, the approach reduces multiple emotional features to presence/absence scores providing both conceptual clarity and efficiency in subsequent data-analytic procedures. The moment-oriented approach has been implemented in multiple datasets, yielding promising results.
A new series of personalized single sessions was developed for Study 2, a randomized controlled trial targeting mild-to-moderate anxiety and depression, to test the effectiveness of customized sessions chosen through detailed data analysis. These sessions focus on psychosocial needs such as emotional stability and self-esteem, identified using a conditional entropy algorithm and EMA data. After completing therapist training and initial piloting, a small group of participants has successfully participated, affirming the method's viability.
In the project's incoming phase at KU Leuven, we recruited 70 high self-criticism participants for a 21-day EMA. Here again, we incorporated person-specific items and applied means ensure a balanced distribution of the target variable. We also included location features derived from participants' GPS. Using these data, we generated person-specific models to predict self-criticism. Using elastic net regression and k-fold cross-validation, we achieved a median AUC of 0.68 though model accuracy varied widely. Despite observing a moderate correlation between the number of items retained and model accuracy, the small sample size limited our ability to identify reliable predictors of accuracy.
Leveraging the person-specific models, we developed a JITAI focused on self-criticism, activating self-compassion exercise prompts based on prior data. Implemented in a micro-randomization design, this system either triggered exercises upon predicting self-criticism or did not, enabling an in-depth efficacy analysis. Despite a small control group for comparison and low real-time exercise usage by some participants, the intervention showed some effects. These results indicate the need for enhanced participant engagement and further data collection emphasizing adherence and intervention engagement.
The results of the project were presented at four scientific conferences, including SPR (twice), APS, and ICPS. Additionally, results were communicated via Twitter, and presented at KU Leuven seminars.
During Study 1's data collection, two smaller projects were launched. The first reviewed the impact of timing on affect dynamics research in a chapter titled ‘A Close Look at the Role of Time in Affect Dynamics Research’. The second analyzed ecological momentary assessment (EMA) data from an open trial, discovering that the differentiation of negative emotions predicts treatment outcomes. This finding, published in Frontiers in Psychology, illustrated the utility of pre-treatment EMA for identifying patient characteristics that forecast treatment success.
Additionally, as part of an effort to characterize emotional states, a new approach to model compound states has been developed – a Moment-Oriented Approach. It employs unsupervised clustering techniques to delineate archetypal patterns of momentary mixtures of emotions. The approach allows the identification of regularities that exist both within individuals and across the sample. Moreover, the approach reduces multiple emotional features to presence/absence scores providing both conceptual clarity and efficiency in subsequent data-analytic procedures. The moment-oriented approach has been implemented in multiple datasets, yielding promising results.
A new series of personalized single sessions was developed for Study 2, a randomized controlled trial targeting mild-to-moderate anxiety and depression, to test the effectiveness of customized sessions chosen through detailed data analysis. These sessions focus on psychosocial needs such as emotional stability and self-esteem, identified using a conditional entropy algorithm and EMA data. After completing therapist training and initial piloting, a small group of participants has successfully participated, affirming the method's viability.
In the project's incoming phase at KU Leuven, we recruited 70 high self-criticism participants for a 21-day EMA. Here again, we incorporated person-specific items and applied means ensure a balanced distribution of the target variable. We also included location features derived from participants' GPS. Using these data, we generated person-specific models to predict self-criticism. Using elastic net regression and k-fold cross-validation, we achieved a median AUC of 0.68 though model accuracy varied widely. Despite observing a moderate correlation between the number of items retained and model accuracy, the small sample size limited our ability to identify reliable predictors of accuracy.
Leveraging the person-specific models, we developed a JITAI focused on self-criticism, activating self-compassion exercise prompts based on prior data. Implemented in a micro-randomization design, this system either triggered exercises upon predicting self-criticism or did not, enabling an in-depth efficacy analysis. Despite a small control group for comparison and low real-time exercise usage by some participants, the intervention showed some effects. These results indicate the need for enhanced participant engagement and further data collection emphasizing adherence and intervention engagement.
The results of the project were presented at four scientific conferences, including SPR (twice), APS, and ICPS. Additionally, results were communicated via Twitter, and presented at KU Leuven seminars.
In recent years, idiographic clinical science has made significant advancements towards creating data-driven, tailored interventions. However, the field still seeks effective methods to fully leverage the potential of personalized psychopathology modeling. This project aimed to bridge this gap by developing and evaluating innovative assessment and intervention tools. The Moment-Oriented Approach, which enables the identification of temporal patterns within multiple continuous data streams, offers valuable insights for both theoretical understanding of psychological phenomena and practical assessment and intervention strategies. Theoretically, it enhances our understanding of psychological dynamics by incorporating concepts of multi-faceted states, along with their durations, frequencies, and transitions. Practically, defining emotional states in terms of presence or absence fits well within the JITAI framework.
The project also acts as a proof-of-concept for the feasibility of data-driven, tailored intervention approaches within clinical populations. Through the implementation of complex measurement designs and innovative intervention models in clinical settings, the project has illuminated key challenges in the field. These include managing the quantity and quality of person-level data, employing effective data modeling techniques, improving the quality and uptake of ecological momentary interventions, and the identification of generalizable features and sub-groups across participants. By not only identifying these challenges but also exploring solutions to them, albeit with partial success, this project marks an important step towards the development of scalable, idiographic, data-driven interventions that could significantly enhance the accessibility of mental health care.
The project also acts as a proof-of-concept for the feasibility of data-driven, tailored intervention approaches within clinical populations. Through the implementation of complex measurement designs and innovative intervention models in clinical settings, the project has illuminated key challenges in the field. These include managing the quantity and quality of person-level data, employing effective data modeling techniques, improving the quality and uptake of ecological momentary interventions, and the identification of generalizable features and sub-groups across participants. By not only identifying these challenges but also exploring solutions to them, albeit with partial success, this project marks an important step towards the development of scalable, idiographic, data-driven interventions that could significantly enhance the accessibility of mental health care.