20 citations · 63 across the 13 of their papers we have counts for
6 papers · 1 filter
IntelligentPooling: Practical Thompson Sampling for mHealth
Sabina Tomkins, Peng Liao, Predrag Klasnja +1
In mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time to a user with the goal of helping the user adopt and maintain healthy behaviors. Reinfo…
Batch Policy Learning in Average Reward Markov Decision Processes
Peng Liao, Zhengling Qi, Runzhe Wan +2
We consider the batch (off-line) policy learning problem in the infinite horizon Markov Decision Process. Motivated by mobile health applications, we focus on learning a policy tha…
The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design Considerations
Ashley E. Walton, Linda M. Collins, Predrag Klasnja +4
Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted such as weekly, daily, or ev…
The Micro-Randomized Trial for Developing Digital Interventions: Data Analysis Methods
Tianchen Qian, Michael A. Russell, Linda M. Collins +4
Although there is much excitement surrounding the use of mobile and wearable technology for the purposes of delivering interventions as people go through their day-to-day lives, da…
Translating Behavioral Theory into Technological Interventions: Case Study of an mHealth App to Increase Self-reporting of Substance-Use Related Data
Mashfiqui Rabbi, Meredith Philyaw-Kotov, Jinseok Li +11
Mobile health (mHealth) applications are a powerful medium for providing behavioral interventions, and systematic reviews suggest that theory-based interventions are more effective…
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data
Sabina Tomkins, Peng Liao, Predrag Klasnja +2
In mobile health (mHealth), reinforcement learning algorithms that adapt to one's context without learning personalized policies might fail to distinguish between the needs of indi…