Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions

Eric B. Hekler, Daniel Rivera, Cesar A. Martin, Sayali S. Phatak, Mohammad T. Freigoun, Elizabeth Korinek, Predrag Klasnja, Marc Adams, Matthew Buman

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

BACKGROUND: Adaptive behavioral interventions are individualized interventions that vary support based on a person's evolving needs. Digital technologies enable these adaptive interventions to function at scale. Adaptive interventions show great promise for producing better results compared with static interventions related to health outcomes. Our central thesis is that adaptive interventions are more likely to succeed at helping individuals meet and maintain behavioral targets if its elements can be iteratively improved via data-driven testing (ie, optimization). Control systems engineering is a discipline focused on decision making in systems that change over time and has a wealth of methods that could be useful for optimizing adaptive interventions. OBJECTIVE: The purpose of this paper was to provide an introductory tutorial on when and what to do when using control systems engineering for designing and optimizing adaptive mobile health (mHealth) behavioral interventions. OVERVIEW: We start with a review of the need for optimization, building on the multiphase optimization strategy (MOST). We then provide an overview of control systems engineering, followed by attributes of problems that are well matched to control engineering. Key steps in the development and optimization of an adaptive intervention from a control engineering perspective are then summarized, with a focus on why, what, and when to do subtasks in each step. IMPLICATIONS: Control engineering offers exciting opportunities for optimizing individualization and adaptation elements of adaptive interventions. Arguably, the time is now for control systems engineers and behavioral and health scientists to partner to advance interventions that can be individualized, adaptive, and scalable. This tutorial should aid in creating the bridge between these communities.

Original languageEnglish (US)
Pages (from-to)e214
JournalJournal of medical Internet research
Volume20
Issue number6
DOIs
StatePublished - Jun 28 2018

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Telemedicine
Health
Decision Making
Technology

Keywords

  • adaptive interventions
  • behavior change
  • behavioral maintenance
  • control systems engineering
  • digital health
  • eHealth
  • mHealth
  • multiphase optimization strategy
  • optimization
  • physical activity

ASJC Scopus subject areas

  • Health Informatics

Cite this

Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions. / Hekler, Eric B.; Rivera, Daniel; Martin, Cesar A.; Phatak, Sayali S.; Freigoun, Mohammad T.; Korinek, Elizabeth; Klasnja, Predrag; Adams, Marc; Buman, Matthew.

In: Journal of medical Internet research, Vol. 20, No. 6, 28.06.2018, p. e214.

Research output: Contribution to journalArticle

Hekler, Eric B. ; Rivera, Daniel ; Martin, Cesar A. ; Phatak, Sayali S. ; Freigoun, Mohammad T. ; Korinek, Elizabeth ; Klasnja, Predrag ; Adams, Marc ; Buman, Matthew. / Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions. In: Journal of medical Internet research. 2018 ; Vol. 20, No. 6. pp. e214.
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