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

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.

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© 2022 The Author(s).

Déclaration de conflit d'intérêts

B.S. is the Founder of SIPPA Solutions as well as the lead principal investigator on behalf of the City University of New York on this research, which is funded by the US National Science Foundation under grant no. 1831214. M.W. is the lead principal investigator on behalf of SIPPA Solutions on this research. A.H. declares no competing interests. J.C. is a co-inventor of the manifold clustering listed in a patent application. The manifold clustering recited in this paper is patent pending on the (US) national and (PCT) international stage.