A mobile health intervention for emerging adults with regular cannabis use: A micro-randomized pilot trial design protocol.

Background: Emerging adult (EA) cannabis use is associated with increased risk for health consequences. Just-in-time adaptive interventions (JITAIs) provide potential for preventing the escalation and consequences of cannabis use. Powered by mobile devices, JITAIs use decision rules that take the person's state and context as input, and output a recommended intervention (e.

Optimizing an adaptive digital oral health intervention for promoting oral self-care behaviors: Micro-randomized trial protocol.

Dental disease continues to be one of the most prevalent chronic diseases in the United States. Although oral self-care behaviors (OSCB), involving systematic twice-a-day tooth brushing, can prevent dental disease, this basic behavior is not sufficiently practiced. Recent advances in digital technology offer tremendous potential for promoting OSCB by delivering Just-In-Time Adaptive Interventions (JITAIs)- interventions that leverage dynamic information about the person's state and context to effectively prompt them to engage in a desired behavior in real-time, real-world settings.

Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care.

While dental disease is largely preventable, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients. Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors. In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors.

Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines.

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g.

Statistical Inference with M-Estimators on Adaptively Collected Data.

Bandit algorithms are increasingly used in real-world sequential decision-making problems. Associated with this is an increased desire to be able to use the resulting datasets to answer scientific questions like: Did one type of ad lead to more purchases? In which contexts is a mobile health intervention effective? However, classical statistical approaches fail to provide valid confidence intervals when used with data collected with bandit algorithms. Alternative methods have recently been developed for simple models (e.

Inference for Batched Bandits.

As bandit algorithms are increasingly utilized in scientific studies and industrial applications, there is an associated increasing need for reliable inference methods based on the resulting adaptively-collected data. In this work, we develop methods for inference on data collected in batches using a bandit algorithm. We first prove that the ordinary least squares estimator (OLS), which is asymptotically normal on independently sampled data, is asymptotically normal on data collected using standard bandit algorithms when there is no unique optimal arm.