Translational Research: Leveraging Spatial Behavior and Longitudinal Activity Space Data for Advancing Mobile Health (mHealth) Interventions

Translational spatial information theory research can improve public health through the incorporationof geographic principles into health interventions. This research presents a novel approach for 1)leveraging spatial behavior as a treatment target to advance behavioral mobile health (mHealth)interventions, and 2) capturing and modeling individual activity space and environmental exposuredata to investigate mHealth intervention efficacy. This approach is demonstrated in the context of arandomized controlled trial of the mHealth intervention PNC-txt (Peer Network Counseling-text),which focuses on social and spatial behaviors as a treatment target, as applied to reducing problematiccannabis use among a sample of over 1,000 young adults with cannabis use disorder (CUD) in theU.S. states Colorado and Tennessee. Survey instruments on each subject’s activity space locations,perceived close friends, and which close friends spend time with the subject at each activity spacelocation, are delivered at baseline, 1 month (at the completion of the intervention), 3 months, and 6months. An example application for the Colorado sample (where adult-use, non-medical cannabislegal) is provided which tests the hypothesis that the indirect effect of PNC-txt on reducing cannabisuse behavior via changes in attitude differs according to shifts in spatial behavior and consequentchange in the activity space exposure to cannabis retailers. Results of moderated mediation modelingsupport the hypothesis; PNC-txt is significantly more effective when accompanied by shifts in spatialbehavior where exposure to cannabis retailers is reduced. Advances in geospatial data and methodshave the potential to inform new knowledge regarding the role of social and environmental contextualfactors in developing more effective mHealth interventions.