32 citations · 67 across the 26 of their papers we have counts for
28 papers
Estimation of Time-Varying Treatment Effects in a Joint Model for Longitudinal and Recurrent Event Outcomes in Mobile Health Data
Madeline R Abbott, Jeremy M G Taylor, Inbal Nahum-Shani +4
Not only does mobile health technology enable researchers to track changes in multiple longitudinal outcomes of interest and to record the occurrence of health-related events over…
Evaluating time-varying treatment effects in hybrid SMART-MRT designs
Mengbing Li, Inbal Nahum-Shani, Walter Dempsey
Recently a new experimental approach, the hybrid experimental design (HED), was introduced to enable investigators to answer scientific questions about building behavioral interven…
Constructing Evidence-Based Tailoring Variables for Adaptive Interventions
John J. Dziak, Inbal Nahum-Shani
Background: Adaptive interventions provide a guide for using ongoing information about individuals to decide whether and how to modify the type, amount, delivery modality, or timin…
"It felt more real": Investigating the User Experience of the MiWaves Personalizing JITAI Pilot Study
Susobhan Ghosh, Pei-Yao Hung, Lara N. Coughlin +6
Cannabis use among emerging adults is increasing globally, posing significant health risks and creating a need for effective interventions. We present an exploratory analysis of th…
Reinforcement Learning on Dyads to Enhance Medication Adherence
Ziping Xu, Hinal Jajal, Sung Won Choi +5
Medication adherence is critical for the recovery of adolescents and young adults (AYAs) who have undergone hematopoietic cell transplantation (HCT). However, maintaining adherence…
A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial
Anna L. Trella, Kelly W. Zhang, Hinal Jajal +4
Dental disease is a prevalent chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases. Despite widespread recomm…
Effective Monitoring of Online Decision-Making Algorithms in Digital Intervention Implementation
Anna L. Trella, Susobhan Ghosh, Erin E. Bonar +10
Online AI decision-making algorithms are increasingly used by digital interventions to dynamically personalize treatment to individuals. These algorithms determine, in real-time, t…
MiWaves Reinforcement Learning Algorithm
Susobhan Ghosh, Yongyi Guo, Pei-Yao Hung +5
The escalating prevalence of cannabis use poses a significant public health challenge globally. In the U.S., cannabis use is more prevalent among emerging adults (EAs) (ages 18-25)…
Oralytics Reinforcement Learning Algorithm
Anna L. Trella, Kelly W. Zhang, Stephanie M. Carpenter +6
Dental disease is still one of the most common chronic diseases in the United States. While dental disease is preventable through healthy oral self-care behaviors (OSCB), this basi…
A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data
Madeline R. Abbott, Walter H. Dempsey, Inbal Nahum-Shani +4
The availability of mobile health (mHealth) technology has enabled increased collection of intensive longitudinal data (ILD). ILD have potential to capture rapid fluctuations in ou…
Data integration methods for micro-randomized trials
Easton Huch, Inbal Nahum-Shani, Lindsey Potter +3
Existing statistical methods for the analysis of micro-randomized trials (MRTs) are designed to estimate causal excursion effects using data from a single MRT. In practice, however…
reBandit: Random Effects based Online RL algorithm for Reducing Cannabis Use
Susobhan Ghosh, Yongyi Guo, Pei-Yao Hung +5
The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally. With a notably wide treatment gap, espe…
Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials
Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani +4
Online reinforcement learning (RL) algorithms offer great potential for personalizing treatment for participants in clinical trials. However, deploying an online, autonomous algori…
Optimal Adaptive SMART Designs with Binary Outcomes
Rik Ghosh, Bibhas Chakraborty, Inbal Nahum-Shani +2
In a sequential multiple-assignment randomized trial (SMART), a sequence of treatments is given to a patient over multiple stages. In each stage, randomization may be done to alloc…
Dyadic Reinforcement Learning
Shuangning Li, Lluis Salvat Niell, Sung Won Choi +3
Mobile health aims to enhance health outcomes by delivering interventions to individuals as they go about their daily life. The involvement of care partners and social support netw…
A Continuous-Time Dynamic Factor Model for Intensive Longitudinal Data Arising from Mobile Health Studies
Madeline R. Abbott, Walter H. Dempsey, Inbal Nahum-Shani +3
Intensive longitudinal data (ILD) collected in mobile health (mHealth) studies contain rich information on multiple outcomes measured frequently over time that have the potential t…
Design of Experiments with Sequential Randomizations on Multiple Timescales: The Hybrid Experimental Design
Inbal Nahum-Shani, John J. Dziak, Hanna Venera +3
Psychological interventions, especially those leveraging mobile and wireless technologies, often include multiple components that are delivered and adapted on multiple timescales (…
Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care
Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani +3
Dental disease is one of the most common chronic diseases despite being largely preventable. However, professional advice on optimal oral hygiene practices is often forgotten or ab…
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines
Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani +3
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in d…
Transformers for prompt-level EMA non-response prediction
Supriya Nagesh, Alexander Moreno, Stephanie M. Carpenter +9
Ecological Momentary Assessments (EMAs) are an important psychological data source for measuring current cognitive states, affect, behavior, and environmental factors from particip…
The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design and Data Analysis Considerations
Tianchen Qian, Ashley E. Walton, Linda M. Collins +8
Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted--weekly, daily, or even many…
Sample size estimation for comparing dynamic treatment regimens in a SMART: a Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes
Jamie Yap, John J. Dziak, Raju Maiti +4
Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to a…
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…
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…