TY - JOUR
T1 - An Individualized, Data-Driven Digital Approach for Precision Behavior Change
AU - Wongvibulsin, Shannon
AU - Martin, Seth S.
AU - Saria, Suchi
AU - Zeger, Scott L.
AU - Murphy, Susan A.
N1 - Funding Information:
This work was supported by the Johns Hopkins School of Medicine Medical Scientist Training Program (National Institutes of Health: Institutional Predoctoral Training Grant T32), the National Institutes of Health (Ruth L. Kirschstein Individual Predoctoral NRSA for MD/PhD: F30 Training Grant), and the Johns Hopkins Individualized Health (inHealth) Initiative. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: SW is supported by the Johns Hopkins School of Medicine Medical Scientist Training Program (National Institutes of Health: Institutional Predoctoral Training Grant - T32), National Institutes of Health: Ruth L. Kirschstein Individual Predoctoral NRSA for MD/PhD: F30 Training Grant, and the Johns Hopkins Individualized Health (inHealth) Initiative.
Publisher Copyright:
© 2019 The Author(s).
PY - 2020/5/1
Y1 - 2020/5/1
N2 - 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.
AB - 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.
KW - behavior change
KW - digital therapeutics
KW - machine learning
KW - mobile health (mHealth)
KW - precision medicine
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U2 - 10.1177/1559827619843489
DO - 10.1177/1559827619843489
M3 - Review article
C2 - 32477031
AN - SCOPUS:85065204572
SN - 1559-8276
VL - 14
SP - 289
EP - 293
JO - American Journal of Lifestyle Medicine
JF - American Journal of Lifestyle Medicine
IS - 3
ER -