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Abstract:
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In mobile health (mHealth) for behavior change and maintenance, interventions are frequent and momentary. Typically, a great deal of information on patient states (e.g., stress), environmental factors, and behavioral responses is generated over time. Such intensive longitudinal data is often collected by self-report or passively with the aid of sensors. One way in which intensive longitudinal intervention data may aid the design of a mobile intervention is the examination of effect moderation; that is, inference about which factors strengthen or weaken the response to just-in-time interventions. In this setting, treatments, outcomes, and candidate moderators are time-varying. This paper introduces a definition for moderated effects in terms of potential outcomes, suitable for intensive longitudinal data, and it develops and compares three estimation strategies (inverse-probability-of-treatment weighting, treatment centering approach, and routine regression) for investigating these moderated effects using primarily standard software. The approach is illustrated using BASICS-Mobile, a smartphone-based intervention designed to curb heavy drinking and smoking among college students.
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