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Related Experiment Video

Updated: Jun 6, 2026

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

Published on: June 20, 2025

Grounding Language Models in Behavioral Science to Scale Physical Activity Interventions for Hispanic/Latinx

Sriya Mantena, Anders Johnson, Narayan Schuetz

Medrxiv : the Preprint Server for Health Sciences

|June 5, 2026

Summary

This summary is machine-generated.

This study developed MHC-Coach-ES, a Spanish-language AI for behavior change, outperforming translated messages in user preference and expert review. This offers a scalable digital health solution for Hispanic/Latinx adults, addressing chronic disease disparities.

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Last Updated: Jun 6, 2026

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

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Published on: June 20, 2025

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Area of Science:

Digital Health
Artificial Intelligence in Healthcare
Behavioral Science

Background:

Hispanic/Latinx populations face higher chronic disease rates due to physical inactivity.
Limited English proficiency hinders access to digital health interventions for over 16 million Hispanic/Latinx adults.
Large language models (LLMs) offer potential for personalized, non-English behavioral coaching, an area yet to be explored.

Purpose of the Study:

Introduce MHC-Coach-ES, a novel Spanish-language LLM fine-tuned on the Transtheoretical Model (TTM) of behavior change.
Evaluate MHC-Coach-ES's effectiveness and user preference compared to a base LLM and translated human-expert messages.
Assess the potential of generative AI for culturally adapted, theory-driven digital health interventions.

Main Methods:

Fine-tuned Llama 3-70B-Instruct using a two-stage pipeline: Spanish health language adaptation and TTM-aligned instruction tuning.
Developed a 2.21-million-token Spanish corpus for health and motivational language.
Translated 3,268 human-written messages for TTM alignment and comparative analysis.

Main Results:

Spanish-speaking participants significantly preferred MHC-Coach-ES messages (81% preference, P <0.001).
MHC-Coach-ES generated more temporally anchored messages (65% vs. 20%) while maintaining readability.
Clinical experts rated MHC-Coach-ES higher for TTM stage alignment (4.83/5) than human-expert messages (4.38/5).

Conclusions:

Generative AI can effectively operationalize behavioral science frameworks in Spanish.
MHC-Coach-ES demonstrates a scalable approach to reducing health disparities through accessible digital interventions.
Personalized, generative AI approaches show promise over translation-based localization for theory-driven health behavior interventions.