Personalized labeling: A strategy for supporting self-medicating patients' decision-making during selection and use

Area of Science:

• Consumer Health Informatics
• Human-Computer Interaction
• Medication Safety

Background:

• Consumer self-medication with over-the-counter (OTC) medications is prevalent.
• Standard Drug Facts Labels (DFLs) are often overlooked, leading to suboptimal medication-use decisions.
• Existing information on DFLs may not adequately guide consumers in selecting appropriate OTCs.

Purpose of the Study:

• To evaluate the effectiveness of a novel 'personalized labeling' strategy for OTC medications.
• To assess if personalized labeling improves consumer decision accuracy and efficiency.
• To compare decision-making performance using personalized labels versus standard DFLs.

Main Methods:

• A proof-of-concept study using a computer-based absolute judgment task.
• Seventy-two participants were divided into a concept-educated group and a control group.
• Participants made binary decisions (yes/no) on OTC appropriateness using standard and personalized labels, with decision accuracy and time recorded.

Main Results:

• The concept-educated group showed significantly higher accuracy and faster decision times with personalized labels compared to standard labels.
• The control group did not exhibit significant differences between personalized and standard labels, highlighting the importance of user education.
• Decision times were faster for 'appropriate for use' (yes) than 'not appropriate for use' (no) decisions.

Conclusions:

• Personalized labeling, when understood by the consumer, can significantly enhance decision-making regarding OTC medication appropriateness.
• The augmented reality interface shows promise for improving medication safety and consumer confidence.
• Further research is needed to validate these findings in real-world settings across diverse populations and product types.