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Behavior Modification01:21

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Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
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Stress Prevention and Stress Management Techniques VI01:30

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Adopting a healthier lifestyle often requires overcoming significant challenges, but leveraging psychological, social, and cultural resources can facilitate meaningful change. Effective self-change hinges on understanding and applying key tools such as motivation and goal setting, which help sustain efforts toward long-term health benefits.
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Impression Management Techniques III: Aligning Actions01:29

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Related Experiment Video

Updated: Apr 12, 2026

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Large Language Model Agents for Improving Engagement with Behavior Change Interventions: Application to Digital

Harsh Kumar1, Suhyeon Yoo1, Angela Zavaleta Bernuy2

  • 1University of Toronto, Canada.

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Summary

Large Language Models (LLMs) can support wellness by mimicking social support for behavior change. An information-providing LLM agent with a friendly persona significantly improved engagement with mindfulness exercises.

Keywords:
behavior changehabit formationlarge language modelsmindfulnesssocial supportsupportive accountabilitywell-being

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Area of Science:

  • Digital health interventions
  • Behavioral science
  • Artificial intelligence in healthcare

Background:

  • Self-directed wellness engagement often declines over time.
  • Traditional social support for behavior change is often inaccessible.
  • Large Language Models (LLMs) show potential for emulating social support.

Purpose of the Study:

  • To assess the impact of LLM agents on user engagement with mindfulness exercises.
  • To explore the effectiveness of information-providing vs. self-reflection-facilitating LLM agents.
  • To investigate LLM-driven social support for sustained behavior change.

Main Methods:

  • Two randomized experiments were conducted.
  • Study 1: A single-session study with 502 crowdworkers.
  • Study 2: A three-week study with 54 participants, comparing two LLM agent types.

Main Results:

  • Both LLM agents increased users' intentions to practice mindfulness.
  • The information-providing LLM agent with a friendly persona significantly improved exercise engagement.
  • LLM agents show promise in bridging the social support gap for digital health.

Conclusions:

  • Specific LLM agent designs can enhance engagement in digital health interventions.
  • LLM-powered social support may be a scalable solution for behavior change.
  • Further research into LLM applications for in situ behavior change is warranted.