Murphy, Susan A.

Designing m-Health interventions for precision mental health support

Bidargaddi, N., Schrader, G., Klasnja, P., Licinio, J., & Murphy, S. (2020) · Translational Psychiatry

Digital Mental Health InterventionsMobile Health and mHealth Applications

Cited by 92

Comparison of a phased experimental approach and a single randomized clinical trial for developing multicomponent behavioral interventions

Collins, L. M., Chakraborty, B., Murphy, S. A., & Strecher, V. (2009) · Clinical Trials

Statistical Methods in Clinical TrialsHealth Policy Implementation Science

Cited by 80

Optimizing Digital Integrated Care via Micro‐Randomized Trials

Walton, A., Nahum‐Shani, I., Crosby, L., Klasnja, P., & Murphy, S. (2018) · Clinical Pharmacology & Therapeutics

Mobile Health and mHealth ApplicationsStatistical Methods in Clinical Trials

Cited by 66

A multiple imputation strategy for sequential multiple assignment randomized trials

Shortreed, S. M., Laber, E., Scott Stroup, T., Pineau, J., & Murphy, S. A. (2014) · Statistics in Medicine

Statistical Methods and Bayesian InferenceStatistical Methods in Clinical Trials

Cited by 61

Translating strategies for promoting engagement in mobile health: A proof-of-concept microrandomized trial.

Nahum-Shani, I., Rabbi, M., Yap, J., Philyaw-Kotov, M. L., Klasnja, P., Bonar, E. E., Cunningham, R. M., Murphy, S. A., & Walton, M. A. (2021) · Health Psychology

Mobile Health and mHealth ApplicationsDigital Mental Health Interventions

Cited by 41

Screening Experiments for Developing Dynamic Treatment Regimes

Murphy, S. A., & Bingham, D. (2009) · Journal of the American Statistical Association

Advanced Causal Inference TechniquesStatistical Methods in Clinical Trials

Cited by 41

Mobile Health: Sensors, Analytic Methods, and Applications

Kumar, S., Murphy, S. A., & Rehg, J. M. (2017) · Springer eBooks

Mobile Health and mHealth ApplicationsContext-Aware Activity Recognition Systems

Cited by 38

The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol

Nahum-Shani, I., Potter, L. N., Lam, C. Y., Yap, J., Moreno, A., Stoffel, R., Wu, Z., Wan, N., Dempsey, W., Kumar, S., et al. (2021) · Contemporary Clinical Trials

Smoking Behavior and CessationMobile Health and mHealth Applications

Cited by 23

Investigating the Impact of Selection Bias in Dose-Response Analyses of Preventive Interventions

Conduct Problems Prevention Research Group*, McGowan, H. M., Nix, R. L., Murphy, S. A., & Bierman, K. L. (2010) · Prevention Science

Advanced Causal Inference TechniquesStatistical Methods in Clinical Trials

Cited by 19

Comment

Almirall, D., Lizotte, D. J., & Murphy, S. A. (2012) · Journal of the American Statistical Association

Statistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

Cited by 19

Optimizing an Acceptance and Commitment Therapy Microintervention Via a Mobile App With Two Cohorts: Protocol for Micro-Randomized Trials

Kroska, E. B., Hoel, S., Victory, A., Murphy, S. A., McInnis, M. G., Stowe, Z. N., & Cochran, A. (2020) · JMIR Research Protocols

Mindfulness and Compassion InterventionsDigital Mental Health Interventions

Cited by 12

Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning

Buçinca, Z., Swaroop, S., Paluch, A. E., Murphy, S. A., & Gajos, K. Z. (2024) · arXiv

Human-Automation Interaction and SafetyReinforcement Learning in Robotics

Cited by 2