Personalized Preventive-Care Pathways through Integrated Behavioral Analytics

Elena V. Park

Personalized Preventive-Care Pathways through Integrated Behavioral Analytics

Author(s)	Dr. Elena V. Park
Country	United States
Abstract	Preventive health care is commonly organized around population-level recommendations, periodic screening, and relatively static risk categories. Although these approaches remain essential, they may not fully capture the changing behavioral conditions through which individual health risks develop between clinical encounters. Advances in mobile sensing, wearable technologies, electronic health records, digital phenotyping, and artificial intelligence now make it possible to observe patterns of physical activity, sedentary behavior, sleep, nutrition, medication-taking routines, physiological signals, and other health-related behaviors with greater temporal resolution. This study examines how integrated behavioral analytics can support personalized preventive-care pathways that identify emerging risk, prioritize modifiable behaviors, deliver context-sensitive interventions, and reassess prevention needs over time. A structured integrative evidence-synthesis methodology was used to examine contemporary research on digital phenotyping, wearable health technologies, artificial intelligence–supported health promotion, personalized digital interventions, and behavioral risk reduction. Recent evidence demonstrates considerable potential but also substantial fragmentation. A 2025 rapid review of 22 artificial intelligence–based health-promotion studies identified interventions targeting dietary behavior, smoking cessation, physical activity, mental health, and preventive care, with several interventions addressing multiple behavioral domains. A separate systematic evidence map included 109 wearable-technology studies involving older adults and identified benefits related to physical-activity monitoring, timely treatment adjustment, patient-centered care, and health-event detection, while noting limited evaluation of acceptability, costs, safety, and sustained real-world use. The present paper proposes an Integrated Behavioral Preventive-Care Pathway in which longitudinal behavioral and physiological signals are converted into individualized risk profiles, clinically interpretable prevention priorities, adaptive recommendations, and monitored feedback loops. The model explicitly retains clinician oversight and incorporates uncertainty, privacy, informed consent, interoperability, accessibility, and algorithmic fairness as implementation requirements. Integrated behavioral analytics should therefore be understood as decision support rather than autonomous medical decision-making. When appropriately validated and governed, such systems may move preventive care from occasional generalized advice toward continuous, personalized, and participatory risk reduction.
Keywords	personalized preventive care; behavioral analytics; digital phenotyping; preventive medicine; wearable technology; artificial intelligence; digital health; behavioral risk; precision prevention; patient-centered care
Field	Engineering
Published In	Volume 1, Issue 3, May-June 2020
Published On	2020-06-28

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American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Personalized Preventive-Care Pathways through Integrated Behavioral Analytics

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