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Grant Details

Grant Number: 5U01CA229445-03 Interpret this number
Primary Investigator: Spruijt-Metz, Donna
Organization: University Of Southern California
Project Title: Operationalizing Behavioral Theory for Mhealth: Dynamics, Context, and Personalization
Fiscal Year: 2020


Unhealthy behaviors contribute to the majority of chronic diseases, which account for 86% of all healthcare spending in the US. Despite a great deal of research, the development of behavior change interventions that are effective, scalable, and sustainable remains challenging. Recent advances in mobile sensing and smartphone-based technologies have led to a novel and promising form of intervention, called a “Just-in-time, adaptive intervention” (JITAI), which has the potential to continuously adapt to changing contexts and personalize to individual needs and opportunities for behavior change. Although interventions have been shown to be more effective when based on sound theory, current behavioral theories lack the temporal granularity and multiscale dynamic structure needed for developing effective JITAIs based on measurements of complex dynamic behaviors and contexts. Simultaneously, there is a lack of modeling frameworks that can express dynamic, temporally multiscale theories and represent dynamic, temporally multiscale data. This project will address the theory-development, measurement, and modeling challenges and opportunities presented by intensively collected longitudinal data, with a focus on physical activity and sedentary behavior, and broad implications for other behaviors. For efficiency, we build on the NIH-funded year-long micro- randomized trial (MRT) of HeartSteps (n=60), an adaptive mHealth intervention based on Social-Cognitive Theory (SCT) developed to increase walking and decrease sedentary behavior in patients with cardiovascular disease. The aims of this new proposal are: 1) Refine and develop dynamic measures of theoretical constructs that influence our target behaviors, 2) Enhance HeartSteps with the measures developed in Aim 1 and collect data from two additional year-long HeartSteps cohorts (sedentary overweight/obese adults (n=60) and type 2 diabetes patients (n=60), total n=180), 3) Develop a modeling framework to operationalize dynamic and contextualized theories of behavior in an intervention setting, and 4) Improve prediction of SCT outcomes using increasingly complex models. The work proposed here will provide new digital, data driven measures of key behavioral theory constructs at the momentary, daily, and weekly time scales, provide new tools tailored for the specification of complex models of behavioral dynamics, as well as new model estimation tools tailored specifically to the complex, longitudinal, multi-time scale behavioral and contextual data that are now accessible using mHealth technologies. Finally, we will leverage the collected data and the proposed modeling tools to develop and test enhanced, dynamic extensions of social cognitive theory operationalized as fully quantified, predictive dynamical models. Collectively, this work will provide the theoretical foundations and tools needed to significantly increase the effectiveness of physical activity-based mobile health interventions over multiple time scales, including their ability to effectively support behavior change over longer time scales. !


Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions.
Authors: Karine K. , Klasnja P. , Murphy S.A. , Marlin B.M. .
Source: Proceedings of machine learning research, 2023 Aug; 216, p. 1047-1057.
PMID: 37724310
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Idiographic Dynamic Modeling for Behavioral Interventions with Mixed Data Partitioning and Discrete Simultaneous Perturbation Stochastic Approximation.
Authors: Kha R.T. , Rivera D.E. , Klasnja P. , Hekler E. .
Source: Proceedings of the ... American Control Conference. American Control Conference, 2023 May-Jun; 2023, p. 283-288.
EPub date: 2023-07-03.
PMID: 37426036
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The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols.
Authors: Chow S.M. , Nahum-Shani I. , Baker J.T. , Spruijt-Metz D. , Allen N.B. , Auerbach R.P. , Dunton G.F. , Friedman N.P. , Intille S.S. , Klasnja P. , et al. .
Source: Translational behavioral medicine, 2023-01-20; 13(1), p. 7-16.
PMID: 36416389
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Survivors' health competence mediates the association between wearable activity tracker use and self-rated health: HINTS analysis.
Authors: De La Torre S. , Spruijt-Metz D. , Farias A.J. .
Source: Journal of cancer survivorship : research and practice, 2022 Dec; 16(6), p. 1268-1278.
EPub date: 2022-01-10.
PMID: 35001258
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BayesLDM: A Domain-specific Modeling Language for Probabilistic Modeling of Longitudinal Data.
Authors: Tung K. , De La Torre S. , El Mistiri M. , Braga De Braganca R. , Hekler E. , Pavel M. , Rivera D. , Klasnja P. , Spruijt-Metz D. , Marlin B.M. .
Source: ...IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, 2022 Nov; 2022, p. 78-90.
EPub date: 2022-12-22.
PMID: 37736024
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Data-driven Interpretable Policy Construction for Personalized Mobile Health.
Authors: Bertsimas D. , Klasnja P. , Murphy S. , Na L. .
Source: 2022 IEEE International Conference on Digital Health (IEEE ICDH 2022) : proceedings : hybrid conference, Barcelona, Spain, 11-15 July 2022. International Conference on Digital Health (2022 : Barcelona, Spain; Online), 2022 Jul; 2022, p. 13-22.
EPub date: 2022-08-24.
PMID: 37965645
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Model Personalization in Behavioral Interventions using Model-on-Demand Estimation and Discrete Simultaneous Perturbation Stochastic Approximation.
Authors: Kha R.T. , Rivera D.E. , Klasnja P. , Hekler E. .
Source: Proceedings of the ... American Control Conference. American Control Conference, 2022 Jun; 2022, p. 671-676.
EPub date: 2022-09-05.
PMID: 36340266
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Enhanced Social Cognitive Theory Dynamic Modeling and Simulation Towards Improving the Estimation of "Just-In-Time" States.
Authors: El Mistiri M. , Rivera D.E. , Klasnja P. , Park J. , Hekler E. .
Source: Proceedings of the ... American Control Conference. American Control Conference, 2022 Jun; 2022, p. 468-473.
EPub date: 2022-09-05.
PMID: 36340265
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Validity and Feasibility of the Monitoring and Modeling Family Eating Dynamics System to Automatically Detect In-field Family Eating Behavior: Observational Study.
Authors: Bell B.M. , Alam R. , Mondol A.S. , Ma M. , Emi I.A. , Preum S.M. , de la Haye K. , Stankovic J.A. , Lach J. , Spruijt-Metz D. .
Source: JMIR mHealth and uHealth, 2022-02-18; 10(2), p. e30211.
EPub date: 2022-02-18.
PMID: 35179508
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Advancing Behavioral Intervention and Theory Development for Mobile Health: The HeartSteps II Protocol.
Authors: Spruijt-Metz D. , Marlin B.M. , Pavel M. , Rivera D.E. , Hekler E. , De La Torre S. , El Mistiri M. , Golaszweski N.M. , Li C. , Braga De Braganca R. , et al. .
Source: International journal of environmental research and public health, 2022-02-17; 19(4), .
EPub date: 2022-02-17.
PMID: 35206455
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Microrandomized Trial Design for Evaluating Just-in-Time Adaptive Interventions Through Mobile Health Technologies for Cardiovascular Disease.
Authors: Golbus J.R. , Dempsey W. , Jackson E.A. , Nallamothu B.K. , Klasnja P. .
Source: Circulation. Cardiovascular quality and outcomes, 2021 Feb; 14(2), p. e006760.
EPub date: 2021-01-12.
PMID: 33430608
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Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study.
Authors: Qian T. , Klasnja P. , Murphy S.A. .
Source: Statistical science : a review journal of the Institute of Mathematical Statistics, 2020; 35(3), p. 375-390.
EPub date: 2020-09-11.
PMID: 33132496
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Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps.
Authors: Klasnja P. , Smith S. , Seewald N.J. , Lee A. , Hall K. , Luers B. , Hekler E.B. , Murphy S.A. .
Source: Annals of behavioral medicine : a publication of the Society of Behavioral Medicine, 2019-05-03; 53(6), p. 573-582.
PMID: 30192907
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Standardized Effect Sizes for Preventive Mobile Health Interventions in Micro-randomized Trials.
Authors: Luers B. , Klasnja P. , Murphy S. .
Source: Prevention science : the official journal of the Society for Prevention Research, 2019 Jan; 20(1), p. 100-109.
PMID: 29318443
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