ArticlePatterns (New York, N.Y.)2022
Personalizing self-management via behavioral predictive analytics with health education for improved self-efficacy.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07032311 (Effect of Behavioral Intervention/Therapy to Reduce Diabetes and Liver Disease Risk Through Prescribed Digital Therapeutics Enabled by AI/ML Powered Predictive Analytics), which is not on this map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Effect of Behavioral Intervention/Therapy to Reduce Diabetes and Liver Disease Risk Through Prescribed Digital Therapeutics Enabled by AI/ML Powered Predictive Analytics
Who cites it
2 citing papers in PubMed, 6 citations in OpenAlex.
- Effectiveness of a digital diabetes self-management education and support program in Vietnamese adults with type 2 diabetes.Scientific reports · 2025Article
- Pathways of Media Contact to Health Literacy in Middle-Aged and Older People: The Chain Mediation Effect of Perceived Social Support and Self-Efficacy.Journal of multidisciplinary healthcare · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.