Personalizing self-management via behavioral predictive analytics with health education for improved self-efficacy

Bon Sy; Michael Wassil; Alisha Hassan; Jin Chen

doi:10.1016/j.patter.2022.100510

Personalizing self-management via behavioral predictive analytics with health education for improved self-efficacy

Patterns (N Y). 2022 May 17;3(6):100510. doi: 10.1016/j.patter.2022.100510. eCollection 2022 Jun 10.

Authors

Bon Sy^{1

2

3}, Michael Wassil³, Alisha Hassan⁴, Jin Chen³

Affiliations

¹ Graduate Center/City University of NY, 365 5th Avenue, NY 10016, USA.
² Queens College/City University of NY, 65-30 Kissena Boulevard, Queens, NY 11367, USA.
³ SIPPA Solutions, 42-06A Bell Boulevard, Queens, NY 11361, USA.
⁴ Hunter College/City University of NY, School of Public Health, New York, NY 10065, USA.

Abstract

The objective of this research is to investigate the feasibility of applying behavioral predictive analytics to optimize diabetes self-management. This research also presents a use case on the application of the anaytics technology platform to deliver an online diabetes prevention program developed by the CDC. The goal of personalized self-management is to affect individuals on behavior change toward actionable health activities on glucose self-monitoring, diet management, and exercise. In conjunction with personalizing self-management, the content of the CDC diabetes prevention program was delivered online directly to a mobile device. The proposed behavioral predictive analytics relies on manifold clustering to identify subpopulations by behavior readiness characteristics exhibiting non-linear properties. Utilizing behavior readiness data of 148 subjects, subpopulations are created using manifold clustering to target personalized actionable health activities. This paper reports the preliminary result of personalizing self-management for 22 subjects under different scenarios and the outcome on improving diabetes self-efficacy of 34 subjects.

Keywords: association patterns; behavioral predictive analytics; diabetes self-efficacy; information-theoretic discretization; manifold clustering; self-health management.