Microrandomized trials: An experimental design for developing just-in-time adaptive interventions.

By Predrag V. Klasnja, Eric B. Hekler, Saul Shiffman, Audrey Boruvka, D. Almirall, Ambuj Tewari, Susan A. Murphy. 2015

635 citations recorded by the source corpus.

Subfields: Applied Psychology, General Health Professions, Statistics and Probability

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Citations in the corpus

Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data (2017, positive): “…some current trends in the mobile health ﬁeld that combines randomized clinical trial with online reinforcement learning through micro-randomized-trials 23 , where the randomization probability can be adopted in an online manner, in analogy to the exploration techniques in reinforcement learning.”
Precision Medicine. (2019, methodology): “A somewhat different approach to precision medicine in mHealth involves using data obtained from a micro-randomized clinical trial developed by (Klasnja et al 2015).”
Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning (2016, background): “…well-studied in the statistical and biomedical literature (Murphy, 2003; Robins, 2004; Moodie et al., 2007; Kosorok and Moodie, 2015; Chakraborty and Moodie, 2013) and furthermore, statistical considerations in mHealth have been studied by, for example, Liao et al. (2015) and Klasnja et al. (2015).”
Adaptive Interventions in Child and Adolescent Mental Health (2016, background): “Often, JITAIs are delivered via smartphone applications (Klasnja et al., 2015).”
Robust Actor-Critic Contextual Bandit for Mobile Health (mHealth) Interventions (2018, background): “The goal of mHealth is to deliver in-time interventions to device users, guiding them to lead healthier lives, such as reducing alcohol abuse [2, 8, 11, 14, 30, 33] and increasing physical activities [1, 20].”
At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study (2018, background): “There have also been innovations in the area of design, referred to as micro-randomization, in which an intervention is randomly assigned to time point (Klasnja et al., 2015).”
An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions (2017, methodology): “For example, consider HeartSteps in which the reward is the number of steps over 30 minutes following time t; there is a generally accepted upper limit on the number of steps a human can take in 30 minutes.”