Assessing Time-Varying Causal Effect Moderation in Mobile Health.

Area of Science:

• Mobile health interventions
• Behavior change and maintenance
• Data analysis for health interventions

Background:

• Mobile health interventions provide real-time treatments for behavior change.
• Data analysis is crucial for developing effective mobile interventions.
• Identifying effect moderators can personalize mobile health treatments.

Purpose of the Study:

• To formally define moderated effects for mobile interventions using potential outcomes.
• To develop and compare methods for estimating moderated effects in mobile health.
• To illustrate the proposed approach using a real-world smartphone intervention.

Main Methods:

• Formal definition of moderated effects in terms of potential outcomes.
• Development and comparison of estimation methods for moderated effects.
• Application of the approach to the BASICS-Mobile intervention.

Main Results:

• A formal definition for moderated effects suitable for mobile interventions was introduced.
• Methods for estimating these moderated effects were developed and evaluated.
• The approach was successfully illustrated using a smartphone-based intervention targeting college students' drinking and smoking behaviors.

Conclusions:

• The proposed framework and methods enhance the development of personalized mobile health interventions.
• This approach allows for a nuanced understanding of how individual characteristics and context influence treatment effectiveness.
• The study provides a valuable tool for optimizing mobile health strategies for behavior change.