健康
心理干预
杠杆(统计)
框架(结构)
背景(考古学)
干预(咨询)
心理学
计算机科学
应用心理学
互联网隐私
业务
医学
护理部
工程类
地理
机器学习
考古
结构工程
作者
Nakyung Kyung,Jason Chan,Sanghee Lim,Byungtae Lee
标识
DOI:10.1287/isre.2020.0119
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI