A brief scale of pathological worry that everyone already has

Mind wandering in anxiety disorders: A status report

Many investigations have targeted the subject of worry in anxiety disorders. Worry can be regarded as a subtype of mind wandering (MW), which is undeliberate, perseverative, negatively-valenced, and mainly future-oriented. Nevertheless, until now only a few studies have explored the role of overall MW in the origin and course of anxiety disorders. To foster progress in this field, we briefly describe and discuss relevant studies addressing MW in subjects with anxiety disorders or symptoms or disorders associated with anxiety symptoms. Provisional synthesis suggests that: a) the overall amount of MW is positively correlated with anxiety symptoms; b) MW characteristics reflecting worry and rumination appear to be relevant in anxiety; c) comorbid depressive and ADHD symptoms may contribute to excessive MW in anxiety; d) MW-related therapeutic interventions may be useful as complementary treatments in anxiety disorders. However, more studies related to MW in anxiety disorders or symptoms are necessary to corroborate and extend these initial findings. Such investigations should ideally combine experience sampling with self-rating assessments of both MW and worry/rumination.

Applying ensemble machine learning models to predict individual response to a digitally delivered worry postponement intervention

OBJECTIVE

Generalized anxiety disorder (GAD) is a prevalent mental health disorder that often goes untreated. A core aspect of GAD is worry, which is associated with negative health outcomes, accentuating a need for simple treatments for worry. The present study leveraged pretreatment individual differences to predict personalized treatment response to a digital intervention.

METHODS

Linear mixed-effect models were used to model changes in daytime and nighttime worry duration and frequency for 163 participants who completed a six-day worry postponement intervention. Ensemble-based machine learning regression and classification models were implemented to predict changes in worry across the intervention. Model feature importance was derived using SHapley Additive exPlanation (SHAP).

RESULTS

Moderate predictive performance was obtained for predicting changes in daytime worry duration (test r2 = 0.221, AUC = 0.77) and nighttime worry frequency (test r2 = 0.164, AUC = 0.72), while poor predictive performance was obtained for nighttime worry duration and daytime worry frequency. Baseline levels of worry and subjective health complaints were most important in driving model predictions.

LIMITATIONS

A complete-case analysis was leveraged to analyze the present data, which was collected from participants that were Dutch and majority female.

CONCLUSIONS

This study suggests that treatment response to a digital intervention for GAD can be accurately predicted using baseline characteristics. Particularly, this worry postponement intervention may be most beneficial for individuals with high baseline worry but fewer subjective health complaints. The present findings highlight the complexities of and need for further research into daily worry dynamics and the personalizable utility of digital interventions.