01_WARN-D Protocol Paper

Protocol paper for WARN-D study (www.WARN-D.com)

WARN-D project hub

This is the project hub for the WARN-D project on building a personalized early warning system for depression (www.WARN-D.com)

Predicting resilience from psychological and physiological daily-life measures

Monitoring well-being with mobile and wearable devices has become an important component for the development of preventive interventions for stress-related psychopathology. Here, we investigated the potential of daily-life psychological and physiological measures from Ecological Momentary Assessments (EMA) and Ecological Physiological Assessments (EPA), as well as their combination, for predicting long-term stress resilience. We operationalized resilience as inverse stressor reactivity (SR) at multiple measurement time points across a six-month period of longitudinal assessments. This allowed us to explicitly separate the contributions from between-subject and within-subject variances in EMA and EPA measures to interindividual differences in SR and intraindividual fluctuations in SR over time. We first used linear mixed models to understand how individual EMA items and EPA features are associated with SR, after which we trained machine learning models (random forest regression) to predict either a participant’s average SR score or their weekly individual SR scores from EMA, EPA or combined EMA and EPA data. We identified significant associations between changes in SR and various psychological and physiological measures from EMA and EPA, respectively – both between-subject and within-subject – suggesting that these measures can be used for monitoring resilience in daily life. We furthermore successfully demonstrate that SR scores can be predicted with moderate accuracy using machine learning models that are trained on EMA data, and that these models perform best when considering within-subject variance by predicting weekly SR scores. Our findings may have implications for future research on daily-life measures and stress resilience, as well as the development of clinical applications targeting the early detection and prevention of stress-related disorders through personalized just-in-time adaptive interventions