Behavioral Modeling in Weight Loss Interventions

35 Pages Posted: 14 Sep 2016 Last revised: 22 Feb 2017

See all articles by Anil Aswani

Anil Aswani

University of California, Berkeley

Philip Kaminsky

University of California, Berkeley - Department of Industrial Engineering & Operations Research (IEOR)

Yonatan Mintz

University of California, Berkeley - Department of Industrial Engineering and Operations Research

Elena Flowers

University of California, San Francisco (UCSF) - School of Nursing

Yoshimi Fukuoka

University of California, San Francisco (UCSF) - School of Nursing

Date Written: September 12, 2016

Abstract

Designing systems with human agents is difficult because it often requires models that characterize agents' responses to changes in the system's states and inputs. An example of this scenario occurs when designing treatments for obesity. While weight loss interventions through increasing physical activity and modifying diet have found success in reducing individuals' weight, such programs are difficult to maintain over long periods of time due to lack of patient adherence. A promising approach to increase adherence is through the personalization of treatments to each patient. In this paper, we make a contribution towards treatment personalization by developing a framework for predictive modeling using utility functions that depend upon both time-varying system states and motivational states evolving according to some modeled process corresponding to qualitative social science models of behavior change. Computing the predictive model requires solving a bilevel program, which we reformulate as a mixed-integer linear program (MILP). This reformulation provides the first (to our knowledge) formulation for Bayesian inference that uses empirical histograms as prior distributions. We study the predictive ability of our framework using a data set from a weight loss intervention, and our predictive model is validated by comparison to standard machine learning approaches. We conclude by describing how our predictive model could be used for optimization, unlike standard machine learning approaches which cannot.

Suggested Citation

Aswani, Anil and Kaminsky, Philip and Mintz, Yonatan and Flowers, Elena and Fukuoka, Yoshimi, Behavioral Modeling in Weight Loss Interventions (September 12, 2016). Available at SSRN: https://ssrn.com/abstract=2838443 or http://dx.doi.org/10.2139/ssrn.2838443

Anil Aswani (Contact Author)

University of California, Berkeley

Berkeley, CA 94720
United States

Philip Kaminsky

University of California, Berkeley - Department of Industrial Engineering & Operations Research (IEOR) ( email )

IEOR Department
4135 Etcheverry Hall
Berkeley, CA 94720
United States

Yonatan Mintz

University of California, Berkeley - Department of Industrial Engineering and Operations Research ( email )

4141 Etcheverry Hall
Berkeley, CA 94720-1777
United States

Elena Flowers

University of California, San Francisco (UCSF) - School of Nursing ( email )

San Francisco, CA 94143
United States

Yoshimi Fukuoka

University of California, San Francisco (UCSF) - School of Nursing ( email )

San Francisco, CA 94143
United States

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