Evaluating visual feedback of experience sampling data for mental health practitioners

The Experience Sampling Method (ESM) allows to capture and quantify people's daily experiences, thoughts, and behaviors in real-world contexts. While ESM holds great promise for informing case conceptualization and improving therapeutic targeting in mental health care, visualizations of self-reported ESM data remain largely tailored to researchers and are often not easy to interpret for mental health practitioners. To what extent such visualizations can assist practitioners within their context of mental health care remains still unclear.

This study investigates the effect of including uncertainty information in visualizations of ESM feedback on practitioners' decision-making processes.

We recruited 40 Belgian mental health practitioners and students in their last year of psychology or educational science studies to take part in an experiment that assessed their proficiency in interpreting ESM feedback based on reality-inspired clinical cases. Three variables (i.e., interactive hover function, error bars, and textual descriptions about effect size and confidence interval) were manipulated in a balanced factorial design to determine their effects on correct data interpretation.

The results indicate that providing textual descriptions about effect size and confidence intervals increased practitioners' confidence levels and accuracies compared to error bars alone. Furthermore, practitioners want to integrate ESM feedback into treatment. When ESM feedback was associated with low confidence, they preferred to further explore the clinical case. In contrast, when ESM feedback was associated with high confidence, they opted for more action-oriented interventions.

These findings underscore the importance of including textual descriptions in graphical visualizations as they help to improve practitioners' decision-making processes in mental health care.