Personalizing self-management via behavioral predictive analytics with health education for improved self-efficacy.
case_series · Level IV
Where this comes from
- Record sourced from PubMed, PMID 35755867.
- Also identified by DOI 10.1016/j.patter.2022.100510 and PMC identifier 9214334.
- Licence recorded as CC BY-NC-ND.
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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.