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Levels of Health Promotion and Illness Prevention

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Related Experiment Video

Updated: May 20, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

AI-Enabled Digital Health Promotion and Prevention: Computational Literature Review.

Mariana Girão Carrilho¹, Diego Costa Pinto¹, Rafael Wagner¹

¹NOVA Information Management School, Universidade Nova de Lisboa, Campus de Campolide, Lisboa, 1070-312, Portugal, 351 213828610.

|May 18, 2026

Summary

Artificial intelligence (AI) is increasingly used in health promotion, but research is fragmented. This study maps AI applications in digital health promotion to better understand their use across different contexts and purposes.

Keywords:

AI artificial intelligence health promotion machine learning natural language processing topic modeling

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Last Updated: May 20, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Area of Science:

* Public Health and Digital Health
* Health Informatics
* Artificial Intelligence in Healthcare

Background:

* Health promotion focuses on empowering individuals and communities for better health and well-being.
* Artificial intelligence (AI) offers potential for timely, personalized, and scalable health promotion interventions.
* Current research on AI in health promotion is fragmented, often categorized by technology rather than purpose, hindering cumulative understanding.

Purpose of the Study:

* To systematically map peer-reviewed research on AI-enabled digital health promotion interventions.
* To clarify the organization of AI techniques by intervention purpose, user, and context.
* To provide a structured overview of AI applications in health promotion.

Main Methods:

* Conducted a large-scale computational literature review of 6328 peer-reviewed articles.
* Utilized natural language processing and unsupervised machine learning for analysis.
* Employed topic modeling and scientometric analyses to identify research structures and patterns.

Main Results:

* Identified three dominant clusters of AI applications in health promotion:
* 1. AI integrated into digital health promotion applications.
* 2. AI systems supporting clinical preventive care and decision-making.
* 3. AI for population-level public health promotion and policy.

Conclusions:

* This study synthesizes AI applications in digital health promotion by context, population, and purpose.
* Findings facilitate comparison of AI interventions beyond their technological form.
* Supports purpose-sensitive design, evaluation, and governance of AI in health promotion.
* Provides a foundation for cumulative research in AI-enabled health promotion.