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

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.

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© 2019 The Author(s).

Déclaration de conflit d'intérêts

Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: SW, SLZ, and SAM declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. SSM serves on the scientific advisory boards of Amgen, Sanofi, Regeneron, Esperion, Novo Nordisk, Quest Diagnostics, and Akcea Therapeutics and reports grants from Apple, Google, iHealth, Nokia, Maryland Innovation Initiative, American Heart Association, Aetna Foundation, P J Schafer Memorial Fund, and David and June Trone Family Foundation. SSM reports a patent pending filed by Johns Hopkins as a co-inventor for a method of LDL-C estimation. SSM is a founder of and holds equity in Corrie Health, which intends to further develop the platform. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies. SS is a founder of and holds equity in Bayesian Health. SS also serves on their board. The results of the study discussed in this publication could affect the value of Bayesian Health. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies.