An Individualized, Data-Driven Digital Approach for Precision Behavior Change

Shannon Wongvibulsin, Seth S. Martin, Suchi Saria, Scott L. Zeger, Susan A. Murphy

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations


Chronic disease now affects approximately half of the US population, causes 7 in 10 deaths, and accounts for roughly 80% of US health care expenditure. Because the root causes of chronic diseases are largely behavioral, effective therapies require frequent, individualized interventions that extend beyond the hospital and clinic to reach patients in their day-to-day lives. However, a mismatch currently exists between what the health care system is equipped to provide and the interventions necessary to effectively address the chronic disease burden. To remedy this health crisis, we present an individualized, data-driven digital approach for chronic disease management and prevention through precision behavior change. The rapid growth of information, biological, and communication technologies makes this an opportune time to develop digital tools that deliver precision interventions for health behavior change to address the chronic disease crisis. Building on this rapid growth, we propose a framework that includes the precise targeting of risk-producing behaviors using real-time sensing technology, machine learning data analysis to identify the most effective intervention, and delivery of that intervention with health-reinforcing feedback to provide real-time, individualized support to empower sustainable health behavior change.

Original languageEnglish (US)
Pages (from-to)289-293
Number of pages5
JournalAmerican Journal of Lifestyle Medicine
Issue number3
StatePublished - May 1 2020


  • behavior change
  • digital therapeutics
  • machine learning
  • mobile health (mHealth)
  • precision medicine

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health Policy
  • Public Health, Environmental and Occupational Health


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