Grant Number:	5R01CA244777-03 Interpret this number
Primary Investigator:	Hekler, Eric
Organization:	University Of California, San Diego
Project Title:	Optimizing Individualized and Adaptive Mhealth Interventions Via Control Systems Engineering Methods
Fiscal Year:	2022

Background: Strong evidence indicates physical activity (PA) reduces risk of bladder, breast, colon, endometrium, esophagus, gastric, and renal cancer, and there is moderate evidence for lung cancer. Individuals aged 40+ who are inactive are at high risk of developing cancers 58,65 but only 1/3 meet guidelines for PA;5-15 thus, they are an important group to target. While effective PA interventions exist, interventions often work only for some individuals or only for a limited time,16-18 thus establishing the need for interventions that can account for dynamic, idiosyncratic PA determinants in order to support each person’s PA. In response, we developed JustWalk, a modular adaptive mobile health (mHealth) intervention that makes daily N-of-1 adjustments to support PA for each person. JustWalk is based on Social Cognitive Theory (SCT) with N-of-1 adaptation driven by a mathematical dynamical model of SCT, which we have developed and validated. JustWalk can perform N-of-1 adaptation based on our innovative use of control engineering methods, which we call a control optimization trial (COT). We have a digital platform and empirical justification for our next step: to evaluate, in a randomized controlled trial (RCT), whether using a COT approach to continuously optimize a PA intervention to each individual is superior to an intervention that is identical but lacks the COT methods. Primary purpose: Evaluate differences in minutes/week of moderate-to-vigorous intensity PA (MVPA) among the COT- optimized vs. non-COT groups at 12 months. Hypotheses: We hypothesize significantly higher minutes/week of MVPA in the intervention arm (COT) relative to control (non-COT) as measured via ActiGraph (powered for effect size of ≥0.32). Methods: We will conduct this RCT with 386 adults aged 40+ who are inactive and overweight/obesity. This is a high-risk group who would benefit from a PA intervention for cancer prevention and who would benefit from an adaptive intervention because of the idiosyncratic and dynamic nature of PA that is pronounced within this group. Assessments will be conducted at baseline, 6, and 12-months using a hip-worn ActiGraph for assessing minutes/week of MVPA, as justified by guidelines. Implications: This research is highly significant because our intervention would be the first scalable PA intervention squarely grounded in SCT with N-of-1 adaptation driven by a mathematical dynamical model version of SCT. Further, favorable results would justify use of our COT methods for other complex and highly idiosyncratic and dynamic behaviors such as weight management, smoking, or substance abuse. Finally, our work should improve understanding of engagement with digital health tools. This research is highly innovative as we would be the first to conduct a COT and to empirically evaluate its utility in an RCT.




Predicting Goal Attainment in Process-Oriented Behavioral Interventions Using a Data-Driven System Identification Approach.
Authors: Banerjee S. , Kha R.T. , Rivera D.E. , Hekler E. .
Source: Journal Of Process Control, 2024 Jul; 139, .
EPub date: 2024-05-27 00:00:00.0.
PMID: 38855126
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The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring.
Authors: Kim M. , Patrick K. , Nebeker C. , Godino J. , Stein S. , Klasnja P. , Perski O. , Viglione C. , Coleman A. , Hekler E. .
Source: Journal Of Medical Internet Research, 2024-03-05 00:00:00.0; 26, p. e49208.
EPub date: 2024-03-05 00:00:00.0.
PMID: 38441954
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The frequency of using wearable activity trackers is associated with minutes of moderate to vigorous physical activity among cancer survivors: Analysis of HINTS data.
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Source: Cancer Epidemiology, 2024 Feb; 88, p. 102491.
EPub date: 2023-12-01 00:00:00.0.
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System Identification and Hybrid Model Predictive Control in Personalized mHealth Interventions for Physical Activity.
Authors: El Mistiri M. , Khan O. , Rivera D.E. , Hekler E. .
Source: Proceedings Of The ... American Control Conference. American Control Conference, 2023 May-Jun; 2023, p. 2240-2245.
EPub date: 2023-07-03 00:00:00.0.
PMID: 37426035
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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 00:00:00.0.
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A Kalman filter-based Hybrid Model Predictive Control Algorithm for Mixed Logical Dynamical Systems: Application to Optimized Interventions for Physical Activity.
Authors: Khan O. , El Mistiri M. , Rivera D.E. , Martin C.A. , Hekler E. .
Source: Proceedings Of The ... Ieee Conference On Decision & Control. Ieee Conference On Decision & Control, 2022 Dec; 2022, p. 2586-2593.
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[A decision framework for an adaptive behavioral intervention for physical activity using hybrid model predictive control: illustration with Just Walk].
Authors: Cevallos D. , Martín C.A. , Mistiri M.E. , Rivera D.E. , Hekler E. .
Source: Revista Iberoamericana De Automatica E Informatica Industrial, 2022-06-29 00:00:00.0; 19(3), p. 297-308.
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Model Predictive Control Strategies for Optimized mHealth Interventions for Physical Activity.
Authors: Mistiri M.E. , Rivera D.E. , Klasnja P. , Park J. , Hekler E. .
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Goal setting and achievement for walking: A series of N-of-1 digital interventions.
Authors: Chevance G. , Baretta D. , Golaszewski N. , Takemoto M. , Shrestha S. , Jain S. , Rivera D.E. , Klasnja P. , Hekler E. .
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Engineering Person-Specific Behavioral Interventions to Promote Physical Activity.
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