Contextual Targeting in mHealth Apps: Harnessing Weather Information and Message Framing to Increase Physical Activity

Mobile technologies provide a unique opportunity for practitioners to identify users’ real-time context and provide personalized interventions to influence their behaviors. However, less is known about a way to improve the effectiveness of mobile health intervention by using context information. This study provides design guidelines on how to use weather information with messaging formats to spur exercise. Through a field experiment that each participant experience different weather conditions in two different treatment periods under the gain or loss interventions, we found that the effects of gain or loss interventions under different weather conditions are heterogeneous. Loss intervention leads to higher fulfillment of exercise goals than gain intervention in sunny weather, whereas gain interventions are more effective than loss interventions in cloudy weather. In addition, we found that weather-based intervention can be used repeatedly over time without losing its effectiveness. Furthermore, we reveal that weather-based intervention is effective toward at-risk populations such as inactive individuals or lower income groups, serving as an mhealth solution that closes the health gap between the haves and have nots. Our findings provide useful guidelines for health service providers and health policymakers regarding how to effectively leverage contextual cues into mobile health intervention. This paper addresses how real-time weather information acquired through mobile technology can be leveraged to enhance the efficacy of mobile interventions for spurring users’ healthier behaviors. Through a field experiment that each participant experience different weather conditions in two different treatment periods under the gain or loss interventions, we found that the effects of gain or loss interventions across sunny and cloudy weather are not uniformly distributed. Loss intervention induces higher levels of fulfillment of exercise goals than gain intervention in sunny weather, whereas gain interventions are more effective than loss interventions in cloudy weather. We also provided empirical evidence to uncover the underlying mechanisms and rules out alternative explanations. The follow-up experiment reveals that weather-based intervention can be used repeatedly over time without losing its effectiveness. Moreover, our result suggests that the observed effect is more evident for people with a lower exercise level and living in areas of lower income. Our study pro