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Related Concept Videos

Self-Schemas02:16

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In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Updated: May 23, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Published on: December 23, 2025

Lessons from Smart Cart 2.0: Considerations for Integrating Purchasing Data into Healthy Eating Interventions with AI

Maya K Vadiveloo1, Alison Tovar2, Anne N Thorndike3

  • 1Department of Nutrition, University of Rhode Island, Fogarty Hall, Kingston, RI, United States.

The Journal of Nutrition
|May 21, 2026
PubMed
Summary
This summary is machine-generated.

Improving digital healthy eating interventions requires responsible implementation of technology. Smart Cart 2.0 offers lessons for leveraging artificial intelligence (AI) and machine learning (ML) while establishing safeguards against misuse.

Keywords:
artificial intelligencedigitalized food environmentsfood purchasingmachine learningpersonalized dietary interventions

Related Experiment Videos

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07:14

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Published on: December 23, 2025

Area of Science:

  • Nutrition Science
  • Health Technology
  • Behavioral Science

Background:

  • Suboptimal diet quality drives chronic diseases, increasing healthcare costs and mortality globally.
  • Dietary behaviors are complex, influenced by biological, psychological, environmental, and social factors, making interventions challenging.
  • Digital technologies in food environments present a dual role, aiding healthy choices yet also exploiting vulnerabilities for unhealthy eating.

Purpose of the Study:

  • To examine lessons from the Smart Cart 2.0 personalized digital healthy eating intervention.
  • To explore the potential and challenges of using artificial intelligence (AI) and machine learning (ML) in dietary behavior change.
  • To advocate for responsible implementation of digital health technologies in nutrition.

Main Methods:

  • The study presents a perspective on the design and implications of Smart Cart 2.0, a digital intervention.
  • It analyzes the use of AI and ML for pattern detection and influencing dietary choices.
  • The approach involves a critical review of technology's role in food environments and health interventions.

Main Results:

  • Smart Cart 2.0 demonstrates the potential of AI/ML for personalized dietary guidance.
  • Digital technologies can be leveraged to improve intervention delivery, personalization, and scalability.
  • The study highlights the need for safeguards against the misuse of technology in promoting unhealthy eating.

Conclusions:

  • Responsible implementation is key to maximizing the benefits of digital dietary interventions.
  • Balancing technological promise with ethical considerations is crucial for effective health technology design.
  • Digital tools offer significant opportunities to enhance public health nutrition strategies when developed and deployed thoughtfully.