Evaluating visual feedback of experience sampling data for mental health practitioners.

Area of Science:

• Psychology
• Mental Health
• Data Visualization

Background:

• Experience Sampling Method (ESM) collects real-world data on daily experiences, thoughts, and behaviors.
• ESM data visualizations are often complex for mental health practitioners, limiting their clinical utility.
• The effectiveness of ESM data visualizations in clinical decision-making is not well understood.

Purpose of the Study:

• To investigate how uncertainty information in ESM data visualizations impacts mental health practitioners' decision-making.
• To assess the influence of interactive features, error bars, and textual descriptions on data interpretation.

Main Methods:

• An experiment was conducted with 40 mental health practitioners and students.
• Participants interpreted clinical cases using ESM feedback with manipulated visualization variables (hover function, error bars, text descriptions).
• A balanced factorial design was used to analyze the impact of each variable on interpretation accuracy.

Main Results:

• Textual descriptions of effect size and confidence intervals significantly improved practitioner accuracy and confidence compared to error bars alone.
• Practitioners expressed a desire to integrate ESM feedback into treatment planning.
• Confidence levels in ESM feedback influenced decision-making: low confidence led to further exploration, high confidence led to action-oriented interventions.

Conclusions:

• Textual descriptions are crucial for enhancing the interpretability of ESM visualizations in mental health care.
• Improved data interpretation through effective visualizations can significantly aid practitioners' decision-making processes.
• Integrating ESM feedback, especially with clear uncertainty indicators, can optimize therapeutic targeting and patient care.